{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a438e8f9",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ebc22ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['font.sans-serif'] = ['simHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26232eb",
   "metadata": {},
   "source": [
    "### 关于y的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb8f4b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <th>Unnamed: 16</th>\n",
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       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
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       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>粤港澳大湾区城市本地生产总值</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>亿元人民币</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
       "      <td>NaN</td>\n",
       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区\\n(亿港元)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门\\n(亿澳门元)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>2056.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1436.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>833.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392.37</td>\n",
       "      <td>...</td>\n",
       "      <td>272.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>514.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>391.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12859.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>523.28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>2505.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2219.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1050.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>439.19</td>\n",
       "      <td>...</td>\n",
       "      <td>345.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>504.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>249.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13375.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>543.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>2841.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2482.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1189.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>478.95</td>\n",
       "      <td>...</td>\n",
       "      <td>404.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>534.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13211.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>551.12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>3203.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2969.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>409.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1328.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>526.57</td>\n",
       "      <td>...</td>\n",
       "      <td>469.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>565.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12973.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>592.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>3758.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3585.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>476.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1578.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>586.46</td>\n",
       "      <td>...</td>\n",
       "      <td>572.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>617.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12566.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>661.47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>4450.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4282.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>551.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1918.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>686.45</td>\n",
       "      <td>...</td>\n",
       "      <td>704.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>695.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>390.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13169.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>853.82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>5187.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5035.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>640.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2450.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>805.11</td>\n",
       "      <td>...</td>\n",
       "      <td>894.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>801.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>420.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14121.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>974.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0 Unnamed: 1  Unnamed: 2 Unnamed: 3  Unnamed: 4 Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市本地生产总值        NaN         NaN        NaN         NaN        NaN   \n",
       "1             NaN        NaN         NaN        NaN         NaN        NaN   \n",
       "2               年         广州         NaN         深圳         NaN         珠海   \n",
       "3            1999    2056.74         NaN    1436.03         NaN     286.61   \n",
       "4            2000    2505.58         NaN     2219.2         NaN     335.92   \n",
       "5            2001    2841.65         NaN    2482.49         NaN     368.34   \n",
       "6            2002    3203.96         NaN    2969.52         NaN     409.04   \n",
       "7            2003    3758.62         NaN    3585.72         NaN     476.71   \n",
       "8            2004    4450.55         NaN    4282.14         NaN     551.68   \n",
       "9            2005    5187.85         NaN    5035.77         NaN     640.53   \n",
       "\n",
       "   Unnamed: 6 Unnamed: 7  Unnamed: 8 Unnamed: 9  ...  Unnamed: 13 Unnamed: 14  \\\n",
       "0         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "1         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "2         NaN         佛山         NaN         惠州  ...           中山         NaN   \n",
       "3         NaN     833.79         NaN     392.37  ...       272.68         NaN   \n",
       "4         NaN    1050.38         NaN     439.19  ...       345.44         NaN   \n",
       "5         NaN    1189.19         NaN     478.95  ...       404.38         NaN   \n",
       "6         NaN    1328.55         NaN     526.57  ...       469.73         NaN   \n",
       "7         NaN    1578.49         NaN     586.46  ...       572.05         NaN   \n",
       "8         NaN    1918.04         NaN     686.45  ...        704.3         NaN   \n",
       "9         NaN    2450.67         NaN     805.11  ...       894.59         NaN   \n",
       "\n",
       "   Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19 Unnamed: 20  \\\n",
       "0          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "1          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "2           江门         NaN           肇庆         NaN  香港特区\\n(亿港元)         NaN   \n",
       "3       514.69         NaN       391.74         NaN     12859.46         NaN   \n",
       "4       504.66         NaN       249.78         NaN     13375.01         NaN   \n",
       "5        534.6         NaN       267.96         NaN     13211.42         NaN   \n",
       "6       565.99         NaN       293.66         NaN     12973.41         NaN   \n",
       "7       617.81         NaN        328.3         NaN     12566.69         NaN   \n",
       "8       695.64         NaN       390.56         NaN     13169.49         NaN   \n",
       "9        801.7         NaN       420.95         NaN     14121.25         NaN   \n",
       "\n",
       "   Unnamed: 21 Unnamed: 22  \n",
       "0          NaN         NaN  \n",
       "1        亿元人民币         NaN  \n",
       "2   澳门\\n(亿澳门元)         NaN  \n",
       "3       523.28         NaN  \n",
       "4       543.69         NaN  \n",
       "5       551.12         NaN  \n",
       "6        592.2         NaN  \n",
       "7       661.47         NaN  \n",
       "8       853.82         NaN  \n",
       "9       974.15         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data1 = pd.read_excel(r\"粤港澳大湾区城市本地生产总值.xls\")\n",
    "data1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7543b8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区\\n(亿港元)</th>\n",
       "      <th>澳门\\n(亿澳门元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>2056.74</td>\n",
       "      <td>1436.03</td>\n",
       "      <td>286.61</td>\n",
       "      <td>833.79</td>\n",
       "      <td>392.37</td>\n",
       "      <td>412.84</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>2505.58</td>\n",
       "      <td>2219.2</td>\n",
       "      <td>335.92</td>\n",
       "      <td>1050.38</td>\n",
       "      <td>439.19</td>\n",
       "      <td>821.14</td>\n",
       "      <td>345.44</td>\n",
       "      <td>504.66</td>\n",
       "      <td>249.78</td>\n",
       "      <td>13375.01</td>\n",
       "      <td>543.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>2841.65</td>\n",
       "      <td>2482.49</td>\n",
       "      <td>368.34</td>\n",
       "      <td>1189.19</td>\n",
       "      <td>478.95</td>\n",
       "      <td>991.89</td>\n",
       "      <td>404.38</td>\n",
       "      <td>534.6</td>\n",
       "      <td>267.96</td>\n",
       "      <td>13211.42</td>\n",
       "      <td>551.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>3203.96</td>\n",
       "      <td>2969.52</td>\n",
       "      <td>409.04</td>\n",
       "      <td>1328.55</td>\n",
       "      <td>526.57</td>\n",
       "      <td>1186.94</td>\n",
       "      <td>469.73</td>\n",
       "      <td>565.99</td>\n",
       "      <td>293.66</td>\n",
       "      <td>12973.41</td>\n",
       "      <td>592.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>3758.62</td>\n",
       "      <td>3585.72</td>\n",
       "      <td>476.71</td>\n",
       "      <td>1578.49</td>\n",
       "      <td>586.46</td>\n",
       "      <td>1452.52</td>\n",
       "      <td>572.05</td>\n",
       "      <td>617.81</td>\n",
       "      <td>328.3</td>\n",
       "      <td>12566.69</td>\n",
       "      <td>661.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年       广州       深圳      珠海       佛山      惠州       东莞      中山      江门  \\\n",
       "0  1999  2056.74  1436.03  286.61   833.79  392.37   412.84  272.68  514.69   \n",
       "1  2000  2505.58   2219.2  335.92  1050.38  439.19   821.14  345.44  504.66   \n",
       "2  2001  2841.65  2482.49  368.34  1189.19  478.95   991.89  404.38   534.6   \n",
       "3  2002  3203.96  2969.52  409.04  1328.55  526.57  1186.94  469.73  565.99   \n",
       "4  2003  3758.62  3585.72  476.71  1578.49  586.46  1452.52  572.05  617.81   \n",
       "\n",
       "2      肇庆 香港特区\\n(亿港元) 澳门\\n(亿澳门元)  \n",
       "0  391.74    12859.46     523.28  \n",
       "1  249.78    13375.01     543.69  \n",
       "2  267.96    13211.42     551.12  \n",
       "3  293.66    12973.41      592.2  \n",
       "4   328.3    12566.69     661.47  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data1.iloc[2]\n",
    "data1 = data1[3:]\n",
    "data1.columns = new_header\n",
    "data1 = data1.reset_index(drop=True)\n",
    "data1 = data1.dropna(thresh=2)\n",
    "data1 = data1.dropna(thresh=3, axis=1)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "047eb7cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>2056.74</td>\n",
       "      <td>1436.03</td>\n",
       "      <td>286.61</td>\n",
       "      <td>833.79</td>\n",
       "      <td>392.37</td>\n",
       "      <td>412.84</td>\n",
       "      <td>272.68</td>\n",
       "      <td>514.69</td>\n",
       "      <td>391.74</td>\n",
       "      <td>11830.7032</td>\n",
       "      <td>470.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>2505.58</td>\n",
       "      <td>2219.2</td>\n",
       "      <td>335.92</td>\n",
       "      <td>1050.38</td>\n",
       "      <td>439.19</td>\n",
       "      <td>821.14</td>\n",
       "      <td>345.44</td>\n",
       "      <td>504.66</td>\n",
       "      <td>249.78</td>\n",
       "      <td>12305.0092</td>\n",
       "      <td>489.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>2841.65</td>\n",
       "      <td>2482.49</td>\n",
       "      <td>368.34</td>\n",
       "      <td>1189.19</td>\n",
       "      <td>478.95</td>\n",
       "      <td>991.89</td>\n",
       "      <td>404.38</td>\n",
       "      <td>534.6</td>\n",
       "      <td>267.96</td>\n",
       "      <td>12154.5064</td>\n",
       "      <td>496.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>3203.96</td>\n",
       "      <td>2969.52</td>\n",
       "      <td>409.04</td>\n",
       "      <td>1328.55</td>\n",
       "      <td>526.57</td>\n",
       "      <td>1186.94</td>\n",
       "      <td>469.73</td>\n",
       "      <td>565.99</td>\n",
       "      <td>293.66</td>\n",
       "      <td>11935.5372</td>\n",
       "      <td>532.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>3758.62</td>\n",
       "      <td>3585.72</td>\n",
       "      <td>476.71</td>\n",
       "      <td>1578.49</td>\n",
       "      <td>586.46</td>\n",
       "      <td>1452.52</td>\n",
       "      <td>572.05</td>\n",
       "      <td>617.81</td>\n",
       "      <td>328.3</td>\n",
       "      <td>11561.3548</td>\n",
       "      <td>595.323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年       广州       深圳      珠海       佛山      惠州       东莞      中山      江门  \\\n",
       "0  1999  2056.74  1436.03  286.61   833.79  392.37   412.84  272.68  514.69   \n",
       "1  2000  2505.58   2219.2  335.92  1050.38  439.19   821.14  345.44  504.66   \n",
       "2  2001  2841.65  2482.49  368.34  1189.19  478.95   991.89  404.38   534.6   \n",
       "3  2002  3203.96  2969.52  409.04  1328.55  526.57  1186.94  469.73  565.99   \n",
       "4  2003  3758.62  3585.72  476.71  1578.49  586.46  1452.52  572.05  617.81   \n",
       "\n",
       "2      肇庆        香港特区       澳门  \n",
       "0  391.74  11830.7032  470.952  \n",
       "1  249.78  12305.0092  489.321  \n",
       "2  267.96  12154.5064  496.008  \n",
       "3  293.66  11935.5372  532.980  \n",
       "4   328.3  11561.3548  595.323  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 汇率\n",
    "HKD_TO_CNY = 0.92\n",
    "MOP_TO_CNY = 0.90\n",
    "\n",
    "# 将香港特区（亿港元）列和澳门（亿澳门元）列的数据转换为人民币\n",
    "data1['香港特区'] = data1['香港特区\\n(亿港元)'].astype(float) * HKD_TO_CNY\n",
    "data1['澳门'] = data1['澳门\\n(亿澳门元)'].astype(float) * MOP_TO_CNY\n",
    "\n",
    "# 删除原有的港元和澳门元列\n",
    "data1.drop(columns=['香港特区\\n(亿港元)', '澳门\\n(亿澳门元)'], inplace=True)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8b070f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2056.74</td>\n",
       "      <td>1436.03</td>\n",
       "      <td>286.61</td>\n",
       "      <td>833.79</td>\n",
       "      <td>392.37</td>\n",
       "      <td>412.84</td>\n",
       "      <td>272.68</td>\n",
       "      <td>514.69</td>\n",
       "      <td>391.74</td>\n",
       "      <td>11830.7032</td>\n",
       "      <td>470.952</td>\n",
       "      <td>18899.1452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2505.58</td>\n",
       "      <td>2219.2</td>\n",
       "      <td>335.92</td>\n",
       "      <td>1050.38</td>\n",
       "      <td>439.19</td>\n",
       "      <td>821.14</td>\n",
       "      <td>345.44</td>\n",
       "      <td>504.66</td>\n",
       "      <td>249.78</td>\n",
       "      <td>12305.0092</td>\n",
       "      <td>489.321</td>\n",
       "      <td>21265.6202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2841.65</td>\n",
       "      <td>2482.49</td>\n",
       "      <td>368.34</td>\n",
       "      <td>1189.19</td>\n",
       "      <td>478.95</td>\n",
       "      <td>991.89</td>\n",
       "      <td>404.38</td>\n",
       "      <td>534.6</td>\n",
       "      <td>267.96</td>\n",
       "      <td>12154.5064</td>\n",
       "      <td>496.008</td>\n",
       "      <td>22209.9644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3203.96</td>\n",
       "      <td>2969.52</td>\n",
       "      <td>409.04</td>\n",
       "      <td>1328.55</td>\n",
       "      <td>526.57</td>\n",
       "      <td>1186.94</td>\n",
       "      <td>469.73</td>\n",
       "      <td>565.99</td>\n",
       "      <td>293.66</td>\n",
       "      <td>11935.5372</td>\n",
       "      <td>532.980</td>\n",
       "      <td>23422.4772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>3758.62</td>\n",
       "      <td>3585.72</td>\n",
       "      <td>476.71</td>\n",
       "      <td>1578.49</td>\n",
       "      <td>586.46</td>\n",
       "      <td>1452.52</td>\n",
       "      <td>572.05</td>\n",
       "      <td>617.81</td>\n",
       "      <td>328.3</td>\n",
       "      <td>11561.3548</td>\n",
       "      <td>595.323</td>\n",
       "      <td>25113.3578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2          广州       深圳      珠海       佛山      惠州       东莞      中山      江门  \\\n",
       "年                                                                          \n",
       "1999  2056.74  1436.03  286.61   833.79  392.37   412.84  272.68  514.69   \n",
       "2000  2505.58   2219.2  335.92  1050.38  439.19   821.14  345.44  504.66   \n",
       "2001  2841.65  2482.49  368.34  1189.19  478.95   991.89  404.38   534.6   \n",
       "2002  3203.96  2969.52  409.04  1328.55  526.57  1186.94  469.73  565.99   \n",
       "2003  3758.62  3585.72  476.71  1578.49  586.46  1452.52  572.05  617.81   \n",
       "\n",
       "2         肇庆        香港特区       澳门      粤港澳大湾区  \n",
       "年                                              \n",
       "1999  391.74  11830.7032  470.952  18899.1452  \n",
       "2000  249.78  12305.0092  489.321  21265.6202  \n",
       "2001  267.96  12154.5064  496.008  22209.9644  \n",
       "2002  293.66  11935.5372  532.980  23422.4772  \n",
       "2003   328.3  11561.3548  595.323  25113.3578  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.set_index('年', inplace=True)\n",
    "data1[\"粤港澳大湾区\"] = data1.sum(axis=1)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35c77842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure(figsize=(12, 6))\n",
    "# 绘制折线图\n",
    "plt.plot(data1.index, data1['粤港澳大湾区'], marker='o', linestyle='-')\n",
    "plt.title('粤港澳大湾区生产总值（亿人民币）变化趋势')\n",
    "plt.xlabel('年份')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('生产总值（亿人民币）')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cebd4d",
   "metadata": {},
   "source": [
    "### x1 货物进出口货值的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5594bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>粤港澳大湾区城市货物进出口货值</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>亿美元</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
       "      <td>NaN</td>\n",
       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>192.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>504.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.51</td>\n",
       "      <td>...</td>\n",
       "      <td>48.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3534.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>233.5046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>639.4433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.6488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.2679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0889</td>\n",
       "      <td>...</td>\n",
       "      <td>60.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.3345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.5055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4146.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>230.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>685.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.29</td>\n",
       "      <td>...</td>\n",
       "      <td>71.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3909.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>279.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>872.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.26</td>\n",
       "      <td>...</td>\n",
       "      <td>93.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4077.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.86</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>349.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.32</td>\n",
       "      <td>...</td>\n",
       "      <td>131.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4556.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>447.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1472.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.35</td>\n",
       "      <td>...</td>\n",
       "      <td>156.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5303.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>534.757249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1827.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257.264491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257.112032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.213184</td>\n",
       "      <td>...</td>\n",
       "      <td>187.513634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.543172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.75668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5888.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Unnamed: 1  Unnamed: 2 Unnamed: 3  Unnamed: 4  Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市货物进出口货值         NaN         NaN        NaN         NaN         NaN   \n",
       "1              NaN         NaN         NaN        NaN         NaN         NaN   \n",
       "2                年          广州         NaN         深圳         NaN          珠海   \n",
       "3             1999      192.16         NaN     504.23         NaN        62.8   \n",
       "4             2000    233.5046         NaN   639.4433         NaN     91.6488   \n",
       "5             2001      230.35         NaN     685.96         NaN       98.02   \n",
       "6             2002      279.23         NaN     872.16         NaN      128.34   \n",
       "7             2003      349.41         NaN       1174         NaN      167.82   \n",
       "8             2004      447.88         NaN    1472.76         NaN      218.01   \n",
       "9             2005  534.757249         NaN    1827.91         NaN  257.264491   \n",
       "\n",
       "   Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  ...  Unnamed: 13  \\\n",
       "0         NaN         NaN         NaN         NaN  ...          NaN   \n",
       "1         NaN         NaN         NaN         NaN  ...          NaN   \n",
       "2         NaN          佛山         NaN          惠州  ...           中山   \n",
       "3         NaN       79.09         NaN       69.51  ...        48.26   \n",
       "4         NaN    103.2679         NaN     82.0889  ...        60.89   \n",
       "5         NaN      110.68         NaN       88.29  ...        71.47   \n",
       "6         NaN       129.7         NaN      112.26  ...        93.41   \n",
       "7         NaN      164.69         NaN      131.32  ...       131.28   \n",
       "8         NaN       216.9         NaN      166.35  ...       156.36   \n",
       "9         NaN  257.112032         NaN  190.213184  ...   187.513634   \n",
       "\n",
       "  Unnamed: 14  Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19  \\\n",
       "0         NaN          NaN         NaN          NaN         NaN          NaN   \n",
       "1         NaN          NaN         NaN          NaN         NaN          NaN   \n",
       "2         NaN           江门         NaN           肇庆         NaN         香港特区   \n",
       "3         NaN        39.72         NaN        10.07         NaN      3534.05   \n",
       "4         NaN      48.3345         NaN      11.5055         NaN      4146.65   \n",
       "5         NaN        43.88         NaN        11.44         NaN       3909.7   \n",
       "6         NaN        47.76         NaN        13.39         NaN      4077.36   \n",
       "7         NaN        58.76         NaN        15.63         NaN      4556.57   \n",
       "8         NaN        78.46         NaN        19.03         NaN      5303.34   \n",
       "9         NaN    90.543172         NaN     21.75668         NaN       5888.7   \n",
       "\n",
       "  Unnamed: 20  Unnamed: 21 Unnamed: 22  \n",
       "0         NaN          NaN         NaN  \n",
       "1         NaN          NaN         亿美元  \n",
       "2         NaN           澳门         NaN  \n",
       "3         NaN         42.4         NaN  \n",
       "4         NaN        47.94         NaN  \n",
       "5         NaN        46.85         NaN  \n",
       "6         NaN        48.86         NaN  \n",
       "7         NaN        53.36         NaN  \n",
       "8         NaN         62.9         NaN  \n",
       "9         NaN        63.87         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data2 = pd.read_excel(r\"粤港澳大湾区城市货物进出口货值.xls\")\n",
    "data2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9fa156d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>192.16</td>\n",
       "      <td>504.23</td>\n",
       "      <td>62.8</td>\n",
       "      <td>79.09</td>\n",
       "      <td>69.51</td>\n",
       "      <td>284.29</td>\n",
       "      <td>48.26</td>\n",
       "      <td>39.72</td>\n",
       "      <td>10.07</td>\n",
       "      <td>3534.05</td>\n",
       "      <td>42.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>233.5046</td>\n",
       "      <td>639.4433</td>\n",
       "      <td>91.6488</td>\n",
       "      <td>103.2679</td>\n",
       "      <td>82.0889</td>\n",
       "      <td>320.2351</td>\n",
       "      <td>60.89</td>\n",
       "      <td>48.3345</td>\n",
       "      <td>11.5055</td>\n",
       "      <td>4146.65</td>\n",
       "      <td>47.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>230.35</td>\n",
       "      <td>685.96</td>\n",
       "      <td>98.02</td>\n",
       "      <td>110.68</td>\n",
       "      <td>88.29</td>\n",
       "      <td>344.52</td>\n",
       "      <td>71.47</td>\n",
       "      <td>43.88</td>\n",
       "      <td>11.44</td>\n",
       "      <td>3909.7</td>\n",
       "      <td>46.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>279.23</td>\n",
       "      <td>872.16</td>\n",
       "      <td>128.34</td>\n",
       "      <td>129.7</td>\n",
       "      <td>112.26</td>\n",
       "      <td>442.41</td>\n",
       "      <td>93.41</td>\n",
       "      <td>47.76</td>\n",
       "      <td>13.39</td>\n",
       "      <td>4077.36</td>\n",
       "      <td>48.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>349.41</td>\n",
       "      <td>1174</td>\n",
       "      <td>167.82</td>\n",
       "      <td>164.69</td>\n",
       "      <td>131.32</td>\n",
       "      <td>520.12</td>\n",
       "      <td>131.28</td>\n",
       "      <td>58.76</td>\n",
       "      <td>15.63</td>\n",
       "      <td>4556.57</td>\n",
       "      <td>53.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年        广州        深圳       珠海        佛山       惠州        东莞      中山  \\\n",
       "0  1999    192.16    504.23     62.8     79.09    69.51    284.29   48.26   \n",
       "1  2000  233.5046  639.4433  91.6488  103.2679  82.0889  320.2351   60.89   \n",
       "2  2001    230.35    685.96    98.02    110.68    88.29    344.52   71.47   \n",
       "3  2002    279.23    872.16   128.34     129.7   112.26    442.41   93.41   \n",
       "4  2003    349.41      1174   167.82    164.69   131.32    520.12  131.28   \n",
       "\n",
       "2       江门       肇庆     香港特区     澳门  \n",
       "0    39.72    10.07  3534.05   42.4  \n",
       "1  48.3345  11.5055  4146.65  47.94  \n",
       "2    43.88    11.44   3909.7  46.85  \n",
       "3    47.76    13.39  4077.36  48.86  \n",
       "4    58.76    15.63  4556.57  53.36  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data2.iloc[2]\n",
    "data2 = data2[3:]\n",
    "data2.columns = new_header\n",
    "data2 = data2.reset_index(drop=True)\n",
    "data2 = data2.dropna(thresh=2)\n",
    "data2 = data2.dropna(thresh=3, axis=1)\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "510d68b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>192.16</td>\n",
       "      <td>504.23</td>\n",
       "      <td>62.8</td>\n",
       "      <td>79.09</td>\n",
       "      <td>69.51</td>\n",
       "      <td>284.29</td>\n",
       "      <td>48.26</td>\n",
       "      <td>39.72</td>\n",
       "      <td>10.07</td>\n",
       "      <td>3534.05</td>\n",
       "      <td>42.4</td>\n",
       "      <td>4866.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>233.5046</td>\n",
       "      <td>639.4433</td>\n",
       "      <td>91.6488</td>\n",
       "      <td>103.2679</td>\n",
       "      <td>82.0889</td>\n",
       "      <td>320.2351</td>\n",
       "      <td>60.89</td>\n",
       "      <td>48.3345</td>\n",
       "      <td>11.5055</td>\n",
       "      <td>4146.65</td>\n",
       "      <td>47.94</td>\n",
       "      <td>5785.5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>230.35</td>\n",
       "      <td>685.96</td>\n",
       "      <td>98.02</td>\n",
       "      <td>110.68</td>\n",
       "      <td>88.29</td>\n",
       "      <td>344.52</td>\n",
       "      <td>71.47</td>\n",
       "      <td>43.88</td>\n",
       "      <td>11.44</td>\n",
       "      <td>3909.7</td>\n",
       "      <td>46.85</td>\n",
       "      <td>5641.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>279.23</td>\n",
       "      <td>872.16</td>\n",
       "      <td>128.34</td>\n",
       "      <td>129.7</td>\n",
       "      <td>112.26</td>\n",
       "      <td>442.41</td>\n",
       "      <td>93.41</td>\n",
       "      <td>47.76</td>\n",
       "      <td>13.39</td>\n",
       "      <td>4077.36</td>\n",
       "      <td>48.86</td>\n",
       "      <td>6244.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>349.41</td>\n",
       "      <td>1174</td>\n",
       "      <td>167.82</td>\n",
       "      <td>164.69</td>\n",
       "      <td>131.32</td>\n",
       "      <td>520.12</td>\n",
       "      <td>131.28</td>\n",
       "      <td>58.76</td>\n",
       "      <td>15.63</td>\n",
       "      <td>4556.57</td>\n",
       "      <td>53.36</td>\n",
       "      <td>7322.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2           广州        深圳       珠海        佛山       惠州        东莞      中山  \\\n",
       "年                                                                        \n",
       "1999    192.16    504.23     62.8     79.09    69.51    284.29   48.26   \n",
       "2000  233.5046  639.4433  91.6488  103.2679  82.0889  320.2351   60.89   \n",
       "2001    230.35    685.96    98.02    110.68    88.29    344.52   71.47   \n",
       "2002    279.23    872.16   128.34     129.7   112.26    442.41   93.41   \n",
       "2003    349.41      1174   167.82    164.69   131.32    520.12  131.28   \n",
       "\n",
       "2          江门       肇庆     香港特区     澳门     粤港澳大湾区  \n",
       "年                                                  \n",
       "1999    39.72    10.07  3534.05   42.4    4866.58  \n",
       "2000  48.3345  11.5055  4146.65  47.94  5785.5086  \n",
       "2001    43.88    11.44   3909.7  46.85    5641.16  \n",
       "2002    47.76    13.39  4077.36  48.86    6244.88  \n",
       "2003    58.76    15.63  4556.57  53.36    7322.96  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.set_index('年', inplace=True)\n",
    "data2[\"粤港澳大湾区\"] = data2.sum(axis=1)\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4e23fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1370.100800</td>\n",
       "      <td>3595.159900</td>\n",
       "      <td>447.764000</td>\n",
       "      <td>563.911700</td>\n",
       "      <td>495.606300</td>\n",
       "      <td>2026.987700</td>\n",
       "      <td>344.0938</td>\n",
       "      <td>283.203600</td>\n",
       "      <td>71.799100</td>\n",
       "      <td>25197.7765</td>\n",
       "      <td>302.3120</td>\n",
       "      <td>34698.715400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1664.887798</td>\n",
       "      <td>4559.230729</td>\n",
       "      <td>653.455944</td>\n",
       "      <td>736.300127</td>\n",
       "      <td>585.293857</td>\n",
       "      <td>2283.276263</td>\n",
       "      <td>434.1457</td>\n",
       "      <td>344.624985</td>\n",
       "      <td>82.034215</td>\n",
       "      <td>29565.6145</td>\n",
       "      <td>341.8122</td>\n",
       "      <td>41250.676318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1642.395500</td>\n",
       "      <td>4890.894800</td>\n",
       "      <td>698.882600</td>\n",
       "      <td>789.148400</td>\n",
       "      <td>629.507700</td>\n",
       "      <td>2456.427600</td>\n",
       "      <td>509.5811</td>\n",
       "      <td>312.864400</td>\n",
       "      <td>81.567200</td>\n",
       "      <td>27876.1610</td>\n",
       "      <td>334.0405</td>\n",
       "      <td>40221.470800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1990.909900</td>\n",
       "      <td>6218.500800</td>\n",
       "      <td>915.064200</td>\n",
       "      <td>924.761000</td>\n",
       "      <td>800.413800</td>\n",
       "      <td>3154.383300</td>\n",
       "      <td>666.0133</td>\n",
       "      <td>340.528800</td>\n",
       "      <td>95.470700</td>\n",
       "      <td>29071.5768</td>\n",
       "      <td>348.3718</td>\n",
       "      <td>44525.994400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2491.293300</td>\n",
       "      <td>8370.620000</td>\n",
       "      <td>1196.556600</td>\n",
       "      <td>1174.239700</td>\n",
       "      <td>936.311600</td>\n",
       "      <td>3708.455600</td>\n",
       "      <td>936.0264</td>\n",
       "      <td>418.958800</td>\n",
       "      <td>111.441900</td>\n",
       "      <td>32488.3441</td>\n",
       "      <td>380.4568</td>\n",
       "      <td>52212.704800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2              广州           深圳           珠海           佛山          惠州  \\\n",
       "年                                                                      \n",
       "1999  1370.100800  3595.159900   447.764000   563.911700  495.606300   \n",
       "2000  1664.887798  4559.230729   653.455944   736.300127  585.293857   \n",
       "2001  1642.395500  4890.894800   698.882600   789.148400  629.507700   \n",
       "2002  1990.909900  6218.500800   915.064200   924.761000  800.413800   \n",
       "2003  2491.293300  8370.620000  1196.556600  1174.239700  936.311600   \n",
       "\n",
       "2              东莞        中山          江门          肇庆        香港特区        澳门  \\\n",
       "年                                                                           \n",
       "1999  2026.987700  344.0938  283.203600   71.799100  25197.7765  302.3120   \n",
       "2000  2283.276263  434.1457  344.624985   82.034215  29565.6145  341.8122   \n",
       "2001  2456.427600  509.5811  312.864400   81.567200  27876.1610  334.0405   \n",
       "2002  3154.383300  666.0133  340.528800   95.470700  29071.5768  348.3718   \n",
       "2003  3708.455600  936.0264  418.958800  111.441900  32488.3441  380.4568   \n",
       "\n",
       "2           粤港澳大湾区  \n",
       "年                   \n",
       "1999  34698.715400  \n",
       "2000  41250.676318  \n",
       "2001  40221.470800  \n",
       "2002  44525.994400  \n",
       "2003  52212.704800  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 汇率\n",
    "USD_TO_CNY = 7.13\n",
    "# 将所有数据从美元转换为人民币\n",
    "data2 = data2.astype(float) * USD_TO_CNY\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c86e4a96",
   "metadata": {},
   "source": [
    "### x2 就业人口的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49230da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>粤港澳大湾区城市就业人口</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>万人</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
       "      <td>NaN</td>\n",
       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>454.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.05</td>\n",
       "      <td>...</td>\n",
       "      <td>110.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>311.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>503.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>308.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.7</td>\n",
       "      <td>...</td>\n",
       "      <td>122.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.53</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>510.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.84</td>\n",
       "      <td>...</td>\n",
       "      <td>124.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>514.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.55</td>\n",
       "      <td>...</td>\n",
       "      <td>131.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.49</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>521.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>422.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206.67</td>\n",
       "      <td>...</td>\n",
       "      <td>144.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>540.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>456.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.92</td>\n",
       "      <td>...</td>\n",
       "      <td>182.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>574.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>576.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.62</td>\n",
       "      <td>...</td>\n",
       "      <td>188.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>333.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 Unnamed: 1  Unnamed: 2 Unnamed: 3  Unnamed: 4 Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市就业人口        NaN         NaN        NaN         NaN        NaN   \n",
       "1           NaN        NaN         NaN        NaN         NaN        NaN   \n",
       "2             年         广州         NaN         深圳         NaN         珠海   \n",
       "3          1999     454.89         NaN          ~         NaN      74.81   \n",
       "4          2000     503.69         NaN      308.5         NaN       78.9   \n",
       "5          2001     510.07         NaN      332.8         NaN       81.8   \n",
       "6          2002     514.08         NaN      359.3         NaN       88.3   \n",
       "7          2003     521.07         NaN     422.29         NaN      88.79   \n",
       "8          2004     540.71         NaN     456.08         NaN       91.4   \n",
       "9          2005     574.46         NaN     576.26         NaN      94.01   \n",
       "\n",
       "   Unnamed: 6 Unnamed: 7  Unnamed: 8 Unnamed: 9  ...  Unnamed: 13 Unnamed: 14  \\\n",
       "0         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "1         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "2         NaN         佛山         NaN         惠州  ...           中山         NaN   \n",
       "3         NaN     192.54         NaN     185.05  ...       110.61         NaN   \n",
       "4         NaN      193.5         NaN      186.7  ...       122.45         NaN   \n",
       "5         NaN     190.44         NaN     196.84  ...       124.28         NaN   \n",
       "6         NaN     205.74         NaN     216.55  ...       131.64         NaN   \n",
       "7         NaN     286.39         NaN     206.67  ...       144.72         NaN   \n",
       "8         NaN     293.51         NaN     212.92  ...       182.98         NaN   \n",
       "9         NaN     348.69         NaN     222.62  ...       188.85         NaN   \n",
       "\n",
       "   Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19 Unnamed: 20  \\\n",
       "0          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "1          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "2           江门         NaN           肇庆         NaN         香港特区         NaN   \n",
       "3       210.34         NaN        199.6         NaN       311.21         NaN   \n",
       "4       208.68         NaN       202.63         NaN       320.73         NaN   \n",
       "5        206.8         NaN       203.94         NaN       325.29         NaN   \n",
       "6       209.99         NaN       204.39         NaN       321.84         NaN   \n",
       "7       202.67         NaN       193.86         NaN       319.06         NaN   \n",
       "8       209.55         NaN       201.34         NaN       327.35         NaN   \n",
       "9       214.53         NaN       215.05         NaN       333.66         NaN   \n",
       "\n",
       "   Unnamed: 21 Unnamed: 22  \n",
       "0          NaN         NaN  \n",
       "1          NaN          万人  \n",
       "2           澳门         NaN  \n",
       "3        19.61         NaN  \n",
       "4        19.53         NaN  \n",
       "5         20.5         NaN  \n",
       "6        20.49         NaN  \n",
       "7        20.54         NaN  \n",
       "8        21.91         NaN  \n",
       "9        23.75         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data3 = pd.read_excel(r\"粤港澳大湾区城市就业人口.xls\")\n",
    "data3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30cb7b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>454.89</td>\n",
       "      <td>~</td>\n",
       "      <td>74.81</td>\n",
       "      <td>192.54</td>\n",
       "      <td>185.05</td>\n",
       "      <td>~</td>\n",
       "      <td>110.61</td>\n",
       "      <td>210.34</td>\n",
       "      <td>199.6</td>\n",
       "      <td>311.21</td>\n",
       "      <td>19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>503.69</td>\n",
       "      <td>308.5</td>\n",
       "      <td>78.9</td>\n",
       "      <td>193.5</td>\n",
       "      <td>186.7</td>\n",
       "      <td>97.88</td>\n",
       "      <td>122.45</td>\n",
       "      <td>208.68</td>\n",
       "      <td>202.63</td>\n",
       "      <td>320.73</td>\n",
       "      <td>19.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>510.07</td>\n",
       "      <td>332.8</td>\n",
       "      <td>81.8</td>\n",
       "      <td>190.44</td>\n",
       "      <td>196.84</td>\n",
       "      <td>100.13</td>\n",
       "      <td>124.28</td>\n",
       "      <td>206.8</td>\n",
       "      <td>203.94</td>\n",
       "      <td>325.29</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>514.08</td>\n",
       "      <td>359.3</td>\n",
       "      <td>88.3</td>\n",
       "      <td>205.74</td>\n",
       "      <td>216.55</td>\n",
       "      <td>104.1</td>\n",
       "      <td>131.64</td>\n",
       "      <td>209.99</td>\n",
       "      <td>204.39</td>\n",
       "      <td>321.84</td>\n",
       "      <td>20.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>521.07</td>\n",
       "      <td>422.29</td>\n",
       "      <td>88.79</td>\n",
       "      <td>286.39</td>\n",
       "      <td>206.67</td>\n",
       "      <td>183.97</td>\n",
       "      <td>144.72</td>\n",
       "      <td>202.67</td>\n",
       "      <td>193.86</td>\n",
       "      <td>319.06</td>\n",
       "      <td>20.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年      广州      深圳     珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "0  1999  454.89       ~  74.81  192.54  185.05       ~  110.61  210.34   \n",
       "1  2000  503.69   308.5   78.9   193.5   186.7   97.88  122.45  208.68   \n",
       "2  2001  510.07   332.8   81.8  190.44  196.84  100.13  124.28   206.8   \n",
       "3  2002  514.08   359.3   88.3  205.74  216.55   104.1  131.64  209.99   \n",
       "4  2003  521.07  422.29  88.79  286.39  206.67  183.97  144.72  202.67   \n",
       "\n",
       "2      肇庆    香港特区     澳门  \n",
       "0   199.6  311.21  19.61  \n",
       "1  202.63  320.73  19.53  \n",
       "2  203.94  325.29   20.5  \n",
       "3  204.39  321.84  20.49  \n",
       "4  193.86  319.06  20.54  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data3.iloc[2]\n",
    "data3 = data3[3:]\n",
    "data3.columns = new_header\n",
    "data3 = data3.reset_index(drop=True)\n",
    "data3 = data3.dropna(thresh=2)\n",
    "data3 = data3.dropna(thresh=3, axis=1)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9bb894e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>503.69</td>\n",
       "      <td>308.5</td>\n",
       "      <td>78.9</td>\n",
       "      <td>193.5</td>\n",
       "      <td>186.7</td>\n",
       "      <td>97.88</td>\n",
       "      <td>122.45</td>\n",
       "      <td>208.68</td>\n",
       "      <td>202.63</td>\n",
       "      <td>320.73</td>\n",
       "      <td>19.53</td>\n",
       "      <td>2243.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>510.07</td>\n",
       "      <td>332.8</td>\n",
       "      <td>81.8</td>\n",
       "      <td>190.44</td>\n",
       "      <td>196.84</td>\n",
       "      <td>100.13</td>\n",
       "      <td>124.28</td>\n",
       "      <td>206.8</td>\n",
       "      <td>203.94</td>\n",
       "      <td>325.29</td>\n",
       "      <td>20.5</td>\n",
       "      <td>2292.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>514.08</td>\n",
       "      <td>359.3</td>\n",
       "      <td>88.3</td>\n",
       "      <td>205.74</td>\n",
       "      <td>216.55</td>\n",
       "      <td>104.1</td>\n",
       "      <td>131.64</td>\n",
       "      <td>209.99</td>\n",
       "      <td>204.39</td>\n",
       "      <td>321.84</td>\n",
       "      <td>20.49</td>\n",
       "      <td>2376.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>521.07</td>\n",
       "      <td>422.29</td>\n",
       "      <td>88.79</td>\n",
       "      <td>286.39</td>\n",
       "      <td>206.67</td>\n",
       "      <td>183.97</td>\n",
       "      <td>144.72</td>\n",
       "      <td>202.67</td>\n",
       "      <td>193.86</td>\n",
       "      <td>319.06</td>\n",
       "      <td>20.54</td>\n",
       "      <td>2590.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>540.71</td>\n",
       "      <td>456.08</td>\n",
       "      <td>91.4</td>\n",
       "      <td>293.51</td>\n",
       "      <td>212.92</td>\n",
       "      <td>303.78</td>\n",
       "      <td>182.98</td>\n",
       "      <td>209.55</td>\n",
       "      <td>201.34</td>\n",
       "      <td>327.35</td>\n",
       "      <td>21.91</td>\n",
       "      <td>2841.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2         广州      深圳     珠海      佛山      惠州      东莞      中山      江门      肇庆  \\\n",
       "年                                                                             \n",
       "2000  503.69   308.5   78.9   193.5   186.7   97.88  122.45  208.68  202.63   \n",
       "2001  510.07   332.8   81.8  190.44  196.84  100.13  124.28   206.8  203.94   \n",
       "2002  514.08   359.3   88.3  205.74  216.55   104.1  131.64  209.99  204.39   \n",
       "2003  521.07  422.29  88.79  286.39  206.67  183.97  144.72  202.67  193.86   \n",
       "2004  540.71  456.08   91.4  293.51  212.92  303.78  182.98  209.55  201.34   \n",
       "\n",
       "2       香港特区     澳门   粤港澳大湾区  \n",
       "年                             \n",
       "2000  320.73  19.53  2243.19  \n",
       "2001  325.29   20.5  2292.89  \n",
       "2002  321.84  20.49  2376.42  \n",
       "2003  319.06  20.54  2590.03  \n",
       "2004  327.35  21.91  2841.53  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.set_index('年', inplace=True)\n",
    "data3 = data3[1:]\n",
    "data3['粤港澳大湾区'] = data3.sum(axis=1)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f0f0c3",
   "metadata": {},
   "source": [
    "### x3 零售业销售额的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a7fcdfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>粤港澳大湾区城市零售业销售额</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>亿元人民币</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
       "      <td>NaN</td>\n",
       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区\\n(亿港元)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门\\n(亿澳门元)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>848.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.39</td>\n",
       "      <td>...</td>\n",
       "      <td>83.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>1079.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>854.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.35</td>\n",
       "      <td>...</td>\n",
       "      <td>156.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>1248.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>832.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>374.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.39</td>\n",
       "      <td>...</td>\n",
       "      <td>158.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>1370.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>941.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>419.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.85</td>\n",
       "      <td>...</td>\n",
       "      <td>178.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>1494.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1095.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>473.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.65</td>\n",
       "      <td>...</td>\n",
       "      <td>201.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>1677.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>555.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213.15</td>\n",
       "      <td>...</td>\n",
       "      <td>234.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>1765.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1757.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>609.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291.29</td>\n",
       "      <td>...</td>\n",
       "      <td>311.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2043.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0 Unnamed: 1  Unnamed: 2 Unnamed: 3  Unnamed: 4 Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市零售业销售额        NaN         NaN        NaN         NaN        NaN   \n",
       "1             NaN        NaN         NaN        NaN         NaN        NaN   \n",
       "2               年         广州         NaN         深圳         NaN         珠海   \n",
       "3            1999     848.25         NaN     368.51         NaN      95.21   \n",
       "4            2000    1079.59         NaN     854.79         NaN     121.33   \n",
       "5            2001    1248.28         NaN     832.04         NaN      135.1   \n",
       "6            2002    1370.68         NaN     941.94         NaN     150.91   \n",
       "7            2003    1494.27         NaN    1095.13         NaN     167.44   \n",
       "8            2004    1677.77         NaN    1250.64         NaN     189.22   \n",
       "9            2005    1765.01         NaN    1757.85         NaN     210.43   \n",
       "\n",
       "   Unnamed: 6 Unnamed: 7  Unnamed: 8 Unnamed: 9  ...  Unnamed: 13 Unnamed: 14  \\\n",
       "0         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "1         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "2         NaN         佛山         NaN         惠州  ...           中山         NaN   \n",
       "3         NaN     257.19         NaN     101.39  ...        83.53         NaN   \n",
       "4         NaN     340.06         NaN     143.35  ...       156.97         NaN   \n",
       "5         NaN     374.85         NaN     141.39  ...       158.76         NaN   \n",
       "6         NaN      419.8         NaN     161.85  ...       178.41         NaN   \n",
       "7         NaN     473.19         NaN     184.65  ...        201.5         NaN   \n",
       "8         NaN     555.75         NaN     213.15  ...       234.61         NaN   \n",
       "9         NaN     609.45         NaN     291.29  ...       311.11         NaN   \n",
       "\n",
       "   Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19 Unnamed: 20  \\\n",
       "0          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "1          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "2           江门         NaN           肇庆         NaN  香港特区\\n(亿港元)         NaN   \n",
       "3       143.16         NaN        93.03         NaN            ~         NaN   \n",
       "4       191.38         NaN        86.31         NaN            ~         NaN   \n",
       "5       197.21         NaN        85.43         NaN            ~         NaN   \n",
       "6       219.89         NaN        96.19         NaN            ~         NaN   \n",
       "7       246.06         NaN       107.72         NaN            ~         NaN   \n",
       "8       275.83         NaN       120.86         NaN            ~         NaN   \n",
       "9       301.36         NaN       162.25         NaN      2043.72         NaN   \n",
       "\n",
       "   Unnamed: 21 Unnamed: 22  \n",
       "0          NaN         NaN  \n",
       "1        亿元人民币         NaN  \n",
       "2   澳门\\n(亿澳门元)         NaN  \n",
       "3            ~         NaN  \n",
       "4        45.94         NaN  \n",
       "5        48.33         NaN  \n",
       "6        52.23         NaN  \n",
       "7        62.68         NaN  \n",
       "8        75.18         NaN  \n",
       "9        87.79         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data4 = pd.read_excel(r\"粤港澳大湾区城市零售业销售额.xls\")\n",
    "data4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53710c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区\\n(亿港元)</th>\n",
       "      <th>澳门\\n(亿澳门元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>848.25</td>\n",
       "      <td>368.51</td>\n",
       "      <td>95.21</td>\n",
       "      <td>257.19</td>\n",
       "      <td>101.39</td>\n",
       "      <td>112.39</td>\n",
       "      <td>83.53</td>\n",
       "      <td>143.16</td>\n",
       "      <td>93.03</td>\n",
       "      <td>~</td>\n",
       "      <td>~</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>1079.59</td>\n",
       "      <td>854.79</td>\n",
       "      <td>121.33</td>\n",
       "      <td>340.06</td>\n",
       "      <td>143.35</td>\n",
       "      <td>271.05</td>\n",
       "      <td>156.97</td>\n",
       "      <td>191.38</td>\n",
       "      <td>86.31</td>\n",
       "      <td>~</td>\n",
       "      <td>45.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>1248.28</td>\n",
       "      <td>832.04</td>\n",
       "      <td>135.1</td>\n",
       "      <td>374.85</td>\n",
       "      <td>141.39</td>\n",
       "      <td>275.71</td>\n",
       "      <td>158.76</td>\n",
       "      <td>197.21</td>\n",
       "      <td>85.43</td>\n",
       "      <td>~</td>\n",
       "      <td>48.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>1370.68</td>\n",
       "      <td>941.94</td>\n",
       "      <td>150.91</td>\n",
       "      <td>419.8</td>\n",
       "      <td>161.85</td>\n",
       "      <td>321.24</td>\n",
       "      <td>178.41</td>\n",
       "      <td>219.89</td>\n",
       "      <td>96.19</td>\n",
       "      <td>~</td>\n",
       "      <td>52.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>1494.27</td>\n",
       "      <td>1095.13</td>\n",
       "      <td>167.44</td>\n",
       "      <td>473.19</td>\n",
       "      <td>184.65</td>\n",
       "      <td>369.78</td>\n",
       "      <td>201.5</td>\n",
       "      <td>246.06</td>\n",
       "      <td>107.72</td>\n",
       "      <td>~</td>\n",
       "      <td>62.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年       广州       深圳      珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "0  1999   848.25   368.51   95.21  257.19  101.39  112.39   83.53  143.16   \n",
       "1  2000  1079.59   854.79  121.33  340.06  143.35  271.05  156.97  191.38   \n",
       "2  2001  1248.28   832.04   135.1  374.85  141.39  275.71  158.76  197.21   \n",
       "3  2002  1370.68   941.94  150.91   419.8  161.85  321.24  178.41  219.89   \n",
       "4  2003  1494.27  1095.13  167.44  473.19  184.65  369.78   201.5  246.06   \n",
       "\n",
       "2      肇庆 香港特区\\n(亿港元) 澳门\\n(亿澳门元)  \n",
       "0   93.03           ~          ~  \n",
       "1   86.31           ~      45.94  \n",
       "2   85.43           ~      48.33  \n",
       "3   96.19           ~      52.23  \n",
       "4  107.72           ~      62.68  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data4.iloc[2]\n",
    "data4 = data4[3:]\n",
    "data4.columns = new_header\n",
    "data4 = data4.reset_index(drop=True)\n",
    "data4 = data4.dropna(thresh=2)\n",
    "data4 = data4.dropna(thresh=3, axis=1)\n",
    "data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b605d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区\\n(亿港元)</th>\n",
       "      <th>澳门\\n(亿澳门元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2005</td>\n",
       "      <td>1765.01</td>\n",
       "      <td>1757.85</td>\n",
       "      <td>210.43</td>\n",
       "      <td>609.45</td>\n",
       "      <td>291.29</td>\n",
       "      <td>608.38</td>\n",
       "      <td>311.11</td>\n",
       "      <td>301.36</td>\n",
       "      <td>162.25</td>\n",
       "      <td>2043.72</td>\n",
       "      <td>87.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006</td>\n",
       "      <td>2199.14</td>\n",
       "      <td>1680.46</td>\n",
       "      <td>255.64</td>\n",
       "      <td>782.06</td>\n",
       "      <td>298.41</td>\n",
       "      <td>599.32</td>\n",
       "      <td>332.23</td>\n",
       "      <td>353.75</td>\n",
       "      <td>169.62</td>\n",
       "      <td>2190.02</td>\n",
       "      <td>106.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>2624.24</td>\n",
       "      <td>1930.81</td>\n",
       "      <td>301.7</td>\n",
       "      <td>957.57</td>\n",
       "      <td>356.06</td>\n",
       "      <td>722.45</td>\n",
       "      <td>397.74</td>\n",
       "      <td>411.93</td>\n",
       "      <td>199.78</td>\n",
       "      <td>2470</td>\n",
       "      <td>141.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>3187.39</td>\n",
       "      <td>2276.59</td>\n",
       "      <td>360.1</td>\n",
       "      <td>1195.75</td>\n",
       "      <td>426.75</td>\n",
       "      <td>881.15</td>\n",
       "      <td>480.29</td>\n",
       "      <td>493.26</td>\n",
       "      <td>238.49</td>\n",
       "      <td>2731.26</td>\n",
       "      <td>193.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2009</td>\n",
       "      <td>3615.77</td>\n",
       "      <td>2567.94</td>\n",
       "      <td>404.46</td>\n",
       "      <td>1408.78</td>\n",
       "      <td>491.1</td>\n",
       "      <td>959.07</td>\n",
       "      <td>549.76</td>\n",
       "      <td>562.07</td>\n",
       "      <td>275.78</td>\n",
       "      <td>2747.42</td>\n",
       "      <td>223.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2      年       广州       深圳      珠海       佛山      惠州      东莞      中山      江门  \\\n",
       "6   2005  1765.01  1757.85  210.43   609.45  291.29  608.38  311.11  301.36   \n",
       "7   2006  2199.14  1680.46  255.64   782.06  298.41  599.32  332.23  353.75   \n",
       "8   2007  2624.24  1930.81   301.7   957.57  356.06  722.45  397.74  411.93   \n",
       "9   2008  3187.39  2276.59   360.1  1195.75  426.75  881.15  480.29  493.26   \n",
       "10  2009  3615.77  2567.94  404.46  1408.78   491.1  959.07  549.76  562.07   \n",
       "\n",
       "2       肇庆 香港特区\\n(亿港元) 澳门\\n(亿澳门元)  \n",
       "6   162.25     2043.72      87.79  \n",
       "7   169.62     2190.02     106.59  \n",
       "8   199.78        2470     141.95  \n",
       "9   238.49     2731.26     193.91  \n",
       "10  275.78     2747.42     223.54  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4 = data4[6:]\n",
    "data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8cfa07b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2005</td>\n",
       "      <td>1765.01</td>\n",
       "      <td>1757.85</td>\n",
       "      <td>210.43</td>\n",
       "      <td>609.45</td>\n",
       "      <td>291.29</td>\n",
       "      <td>608.38</td>\n",
       "      <td>311.11</td>\n",
       "      <td>301.36</td>\n",
       "      <td>162.25</td>\n",
       "      <td>1880.2224</td>\n",
       "      <td>79.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006</td>\n",
       "      <td>2199.14</td>\n",
       "      <td>1680.46</td>\n",
       "      <td>255.64</td>\n",
       "      <td>782.06</td>\n",
       "      <td>298.41</td>\n",
       "      <td>599.32</td>\n",
       "      <td>332.23</td>\n",
       "      <td>353.75</td>\n",
       "      <td>169.62</td>\n",
       "      <td>2014.8184</td>\n",
       "      <td>95.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>2624.24</td>\n",
       "      <td>1930.81</td>\n",
       "      <td>301.7</td>\n",
       "      <td>957.57</td>\n",
       "      <td>356.06</td>\n",
       "      <td>722.45</td>\n",
       "      <td>397.74</td>\n",
       "      <td>411.93</td>\n",
       "      <td>199.78</td>\n",
       "      <td>2272.4000</td>\n",
       "      <td>127.755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>3187.39</td>\n",
       "      <td>2276.59</td>\n",
       "      <td>360.1</td>\n",
       "      <td>1195.75</td>\n",
       "      <td>426.75</td>\n",
       "      <td>881.15</td>\n",
       "      <td>480.29</td>\n",
       "      <td>493.26</td>\n",
       "      <td>238.49</td>\n",
       "      <td>2512.7592</td>\n",
       "      <td>174.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2009</td>\n",
       "      <td>3615.77</td>\n",
       "      <td>2567.94</td>\n",
       "      <td>404.46</td>\n",
       "      <td>1408.78</td>\n",
       "      <td>491.1</td>\n",
       "      <td>959.07</td>\n",
       "      <td>549.76</td>\n",
       "      <td>562.07</td>\n",
       "      <td>275.78</td>\n",
       "      <td>2527.6264</td>\n",
       "      <td>201.186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2      年       广州       深圳      珠海       佛山      惠州      东莞      中山      江门  \\\n",
       "6   2005  1765.01  1757.85  210.43   609.45  291.29  608.38  311.11  301.36   \n",
       "7   2006  2199.14  1680.46  255.64   782.06  298.41  599.32  332.23  353.75   \n",
       "8   2007  2624.24  1930.81   301.7   957.57  356.06  722.45  397.74  411.93   \n",
       "9   2008  3187.39  2276.59   360.1  1195.75  426.75  881.15  480.29  493.26   \n",
       "10  2009  3615.77  2567.94  404.46  1408.78   491.1  959.07  549.76  562.07   \n",
       "\n",
       "2       肇庆       香港特区       澳门  \n",
       "6   162.25  1880.2224   79.011  \n",
       "7   169.62  2014.8184   95.931  \n",
       "8   199.78  2272.4000  127.755  \n",
       "9   238.49  2512.7592  174.519  \n",
       "10  275.78  2527.6264  201.186  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 汇率\n",
    "HKD_TO_CNY = 0.92\n",
    "MOP_TO_CNY = 0.90\n",
    "\n",
    "# 将香港特区（亿港元）列和澳门（亿澳门元）列的数据转换为人民币\n",
    "data4['香港特区'] = data4['香港特区\\n(亿港元)'].astype(float) * HKD_TO_CNY\n",
    "data4['澳门'] = data4['澳门\\n(亿澳门元)'].astype(float) * MOP_TO_CNY\n",
    "\n",
    "# 删除原有的港元和澳门元列\n",
    "data4.drop(columns=['香港特区\\n(亿港元)', '澳门\\n(亿澳门元)'], inplace=True)\n",
    "data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f81c370c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1765.01</td>\n",
       "      <td>1757.85</td>\n",
       "      <td>210.43</td>\n",
       "      <td>609.45</td>\n",
       "      <td>291.29</td>\n",
       "      <td>608.38</td>\n",
       "      <td>311.11</td>\n",
       "      <td>301.36</td>\n",
       "      <td>162.25</td>\n",
       "      <td>1880.2224</td>\n",
       "      <td>79.011</td>\n",
       "      <td>7976.3634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2199.14</td>\n",
       "      <td>1680.46</td>\n",
       "      <td>255.64</td>\n",
       "      <td>782.06</td>\n",
       "      <td>298.41</td>\n",
       "      <td>599.32</td>\n",
       "      <td>332.23</td>\n",
       "      <td>353.75</td>\n",
       "      <td>169.62</td>\n",
       "      <td>2014.8184</td>\n",
       "      <td>95.931</td>\n",
       "      <td>8781.3794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2624.24</td>\n",
       "      <td>1930.81</td>\n",
       "      <td>301.7</td>\n",
       "      <td>957.57</td>\n",
       "      <td>356.06</td>\n",
       "      <td>722.45</td>\n",
       "      <td>397.74</td>\n",
       "      <td>411.93</td>\n",
       "      <td>199.78</td>\n",
       "      <td>2272.4000</td>\n",
       "      <td>127.755</td>\n",
       "      <td>10302.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>3187.39</td>\n",
       "      <td>2276.59</td>\n",
       "      <td>360.1</td>\n",
       "      <td>1195.75</td>\n",
       "      <td>426.75</td>\n",
       "      <td>881.15</td>\n",
       "      <td>480.29</td>\n",
       "      <td>493.26</td>\n",
       "      <td>238.49</td>\n",
       "      <td>2512.7592</td>\n",
       "      <td>174.519</td>\n",
       "      <td>12227.0482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>3615.77</td>\n",
       "      <td>2567.94</td>\n",
       "      <td>404.46</td>\n",
       "      <td>1408.78</td>\n",
       "      <td>491.1</td>\n",
       "      <td>959.07</td>\n",
       "      <td>549.76</td>\n",
       "      <td>562.07</td>\n",
       "      <td>275.78</td>\n",
       "      <td>2527.6264</td>\n",
       "      <td>201.186</td>\n",
       "      <td>13563.5424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2          广州       深圳      珠海       佛山      惠州      东莞      中山      江门  \\\n",
       "年                                                                         \n",
       "2005  1765.01  1757.85  210.43   609.45  291.29  608.38  311.11  301.36   \n",
       "2006  2199.14  1680.46  255.64   782.06  298.41  599.32  332.23  353.75   \n",
       "2007  2624.24  1930.81   301.7   957.57  356.06  722.45  397.74  411.93   \n",
       "2008  3187.39  2276.59   360.1  1195.75  426.75  881.15  480.29  493.26   \n",
       "2009  3615.77  2567.94  404.46  1408.78   491.1  959.07  549.76  562.07   \n",
       "\n",
       "2         肇庆       香港特区       澳门      粤港澳大湾区  \n",
       "年                                             \n",
       "2005  162.25  1880.2224   79.011   7976.3634  \n",
       "2006  169.62  2014.8184   95.931   8781.3794  \n",
       "2007  199.78  2272.4000  127.755   10302.435  \n",
       "2008  238.49  2512.7592  174.519  12227.0482  \n",
       "2009  275.78  2527.6264  201.186  13563.5424  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.set_index('年', inplace=True)\n",
    "data4[\"粤港澳大湾区\"] = data4.sum(axis=1)\n",
    "data4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc985bef",
   "metadata": {},
   "source": [
    "### x4 留宿旅客的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae60200f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>万人次</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
       "      <td>NaN</td>\n",
       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>2115.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>922.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.81</td>\n",
       "      <td>...</td>\n",
       "      <td>312.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>472.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>353.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>2299.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1325.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>382.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>483.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214.28</td>\n",
       "      <td>...</td>\n",
       "      <td>372.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>501.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>389.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>519.67</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>2511.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1455.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.03</td>\n",
       "      <td>...</td>\n",
       "      <td>396.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>544.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>2069.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1031.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>425.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.26</td>\n",
       "      <td>...</td>\n",
       "      <td>332.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>517.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>656.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>2369.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1448.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>438.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>271.62</td>\n",
       "      <td>...</td>\n",
       "      <td>386.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>429.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>630.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>2674.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1942.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>561.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>548.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>352.01</td>\n",
       "      <td>...</td>\n",
       "      <td>430.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>473.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>832.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>2850.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2142.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>640.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>571.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>459.86</td>\n",
       "      <td>...</td>\n",
       "      <td>470.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>901.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 Unnamed: 1  Unnamed: 2 Unnamed: 3  Unnamed: 4 Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市留宿旅客        NaN         NaN        NaN         NaN        NaN   \n",
       "1           NaN        NaN         NaN        NaN         NaN        NaN   \n",
       "2             年         广州         NaN         深圳         NaN         珠海   \n",
       "3          1999    2115.92         NaN     922.23         NaN     335.75   \n",
       "4          2000    2299.96         NaN    1325.33         NaN     382.93   \n",
       "5          2001    2511.68         NaN    1455.57         NaN     428.98   \n",
       "6          2002    2069.31         NaN     1031.7         NaN     319.07   \n",
       "7          2003    2369.82         NaN    1448.82         NaN     438.19   \n",
       "8          2004    2674.71         NaN    1942.93         NaN     561.18   \n",
       "9          2005    2850.56         NaN    2142.83         NaN     640.19   \n",
       "\n",
       "   Unnamed: 6 Unnamed: 7  Unnamed: 8 Unnamed: 9  ...  Unnamed: 13 Unnamed: 14  \\\n",
       "0         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "1         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "2         NaN         佛山         NaN         惠州  ...           中山         NaN   \n",
       "3         NaN     465.65         NaN     199.81  ...       312.71         NaN   \n",
       "4         NaN     483.04         NaN     214.28  ...       372.43         NaN   \n",
       "5         NaN     491.06         NaN     247.03  ...       396.57         NaN   \n",
       "6         NaN     425.54         NaN     219.26  ...       332.75         NaN   \n",
       "7         NaN     364.48         NaN     271.62  ...       386.61         NaN   \n",
       "8         NaN     548.78         NaN     352.01  ...       430.09         NaN   \n",
       "9         NaN     571.21         NaN     459.86  ...        470.4         NaN   \n",
       "\n",
       "   Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19 Unnamed: 20  \\\n",
       "0          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "1          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "2           江门         NaN           肇庆         NaN         香港特区         NaN   \n",
       "3       472.94         NaN       353.49         NaN            ~         NaN   \n",
       "4       501.66         NaN       389.14         NaN            ~         NaN   \n",
       "5       544.64         NaN       491.06         NaN            ~         NaN   \n",
       "6       517.23         NaN       412.86         NaN            ~         NaN   \n",
       "7       500.41         NaN       429.84         NaN            ~         NaN   \n",
       "8       540.55         NaN       473.76         NaN            ~         NaN   \n",
       "9       575.25         NaN       512.45         NaN            ~         NaN   \n",
       "\n",
       "   Unnamed: 21 Unnamed: 22  \n",
       "0          NaN         NaN  \n",
       "1          NaN         万人次  \n",
       "2           澳门         NaN  \n",
       "3            ~         NaN  \n",
       "4       519.67         NaN  \n",
       "5       584.18         NaN  \n",
       "6       656.52         NaN  \n",
       "7       630.85         NaN  \n",
       "8       832.34         NaN  \n",
       "9       901.41         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data5 = pd.read_excel(r\"粤港澳大湾区城市留宿旅客.xls\")\n",
    "data5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7bd9891e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>2115.92</td>\n",
       "      <td>922.23</td>\n",
       "      <td>335.75</td>\n",
       "      <td>465.65</td>\n",
       "      <td>199.81</td>\n",
       "      <td>251.99</td>\n",
       "      <td>312.71</td>\n",
       "      <td>472.94</td>\n",
       "      <td>353.49</td>\n",
       "      <td>~</td>\n",
       "      <td>~</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>2299.96</td>\n",
       "      <td>1325.33</td>\n",
       "      <td>382.93</td>\n",
       "      <td>483.04</td>\n",
       "      <td>214.28</td>\n",
       "      <td>275.92</td>\n",
       "      <td>372.43</td>\n",
       "      <td>501.66</td>\n",
       "      <td>389.14</td>\n",
       "      <td>~</td>\n",
       "      <td>519.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>2511.68</td>\n",
       "      <td>1455.57</td>\n",
       "      <td>428.98</td>\n",
       "      <td>491.06</td>\n",
       "      <td>247.03</td>\n",
       "      <td>301.61</td>\n",
       "      <td>396.57</td>\n",
       "      <td>544.64</td>\n",
       "      <td>491.06</td>\n",
       "      <td>~</td>\n",
       "      <td>584.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>2069.31</td>\n",
       "      <td>1031.7</td>\n",
       "      <td>319.07</td>\n",
       "      <td>425.54</td>\n",
       "      <td>219.26</td>\n",
       "      <td>244.97</td>\n",
       "      <td>332.75</td>\n",
       "      <td>517.23</td>\n",
       "      <td>412.86</td>\n",
       "      <td>~</td>\n",
       "      <td>656.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>2369.82</td>\n",
       "      <td>1448.82</td>\n",
       "      <td>438.19</td>\n",
       "      <td>364.48</td>\n",
       "      <td>271.62</td>\n",
       "      <td>705.41</td>\n",
       "      <td>386.61</td>\n",
       "      <td>500.41</td>\n",
       "      <td>429.84</td>\n",
       "      <td>~</td>\n",
       "      <td>630.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年       广州       深圳      珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "0  1999  2115.92   922.23  335.75  465.65  199.81  251.99  312.71  472.94   \n",
       "1  2000  2299.96  1325.33  382.93  483.04  214.28  275.92  372.43  501.66   \n",
       "2  2001  2511.68  1455.57  428.98  491.06  247.03  301.61  396.57  544.64   \n",
       "3  2002  2069.31   1031.7  319.07  425.54  219.26  244.97  332.75  517.23   \n",
       "4  2003  2369.82  1448.82  438.19  364.48  271.62  705.41  386.61  500.41   \n",
       "\n",
       "2      肇庆 香港特区      澳门  \n",
       "0  353.49    ~       ~  \n",
       "1  389.14    ~  519.67  \n",
       "2  491.06    ~  584.18  \n",
       "3  412.86    ~  656.52  \n",
       "4  429.84    ~  630.85  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data5.iloc[2]\n",
    "data5 = data5[3:]\n",
    "data5.columns = new_header\n",
    "data5 = data5.reset_index(drop=True)\n",
    "data5 = data5.dropna(thresh=2)\n",
    "data5 = data5.dropna(thresh=3, axis=1)\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8851817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>2299.96</td>\n",
       "      <td>1325.33</td>\n",
       "      <td>382.93</td>\n",
       "      <td>483.04</td>\n",
       "      <td>214.28</td>\n",
       "      <td>275.92</td>\n",
       "      <td>372.43</td>\n",
       "      <td>501.66</td>\n",
       "      <td>389.14</td>\n",
       "      <td>519.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>2511.68</td>\n",
       "      <td>1455.57</td>\n",
       "      <td>428.98</td>\n",
       "      <td>491.06</td>\n",
       "      <td>247.03</td>\n",
       "      <td>301.61</td>\n",
       "      <td>396.57</td>\n",
       "      <td>544.64</td>\n",
       "      <td>491.06</td>\n",
       "      <td>584.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>2069.31</td>\n",
       "      <td>1031.7</td>\n",
       "      <td>319.07</td>\n",
       "      <td>425.54</td>\n",
       "      <td>219.26</td>\n",
       "      <td>244.97</td>\n",
       "      <td>332.75</td>\n",
       "      <td>517.23</td>\n",
       "      <td>412.86</td>\n",
       "      <td>656.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>2369.82</td>\n",
       "      <td>1448.82</td>\n",
       "      <td>438.19</td>\n",
       "      <td>364.48</td>\n",
       "      <td>271.62</td>\n",
       "      <td>705.41</td>\n",
       "      <td>386.61</td>\n",
       "      <td>500.41</td>\n",
       "      <td>429.84</td>\n",
       "      <td>630.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004</td>\n",
       "      <td>2674.71</td>\n",
       "      <td>1942.93</td>\n",
       "      <td>561.18</td>\n",
       "      <td>548.78</td>\n",
       "      <td>352.01</td>\n",
       "      <td>712.86</td>\n",
       "      <td>430.09</td>\n",
       "      <td>540.55</td>\n",
       "      <td>473.76</td>\n",
       "      <td>832.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年       广州       深圳      珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "1  2000  2299.96  1325.33  382.93  483.04  214.28  275.92  372.43  501.66   \n",
       "2  2001  2511.68  1455.57  428.98  491.06  247.03  301.61  396.57  544.64   \n",
       "3  2002  2069.31   1031.7  319.07  425.54  219.26  244.97  332.75  517.23   \n",
       "4  2003  2369.82  1448.82  438.19  364.48  271.62  705.41  386.61  500.41   \n",
       "5  2004  2674.71  1942.93  561.18  548.78  352.01  712.86  430.09  540.55   \n",
       "\n",
       "2      肇庆      澳门  \n",
       "1  389.14  519.67  \n",
       "2  491.06  584.18  \n",
       "3  412.86  656.52  \n",
       "4  429.84  630.85  \n",
       "5  473.76  832.34  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5 = data5.drop(columns=['香港特区'])\n",
    "data5 = data5[1:]\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "427c054c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2299.96</td>\n",
       "      <td>1325.33</td>\n",
       "      <td>382.93</td>\n",
       "      <td>483.04</td>\n",
       "      <td>214.28</td>\n",
       "      <td>275.92</td>\n",
       "      <td>372.43</td>\n",
       "      <td>501.66</td>\n",
       "      <td>389.14</td>\n",
       "      <td>519.67</td>\n",
       "      <td>6764.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2511.68</td>\n",
       "      <td>1455.57</td>\n",
       "      <td>428.98</td>\n",
       "      <td>491.06</td>\n",
       "      <td>247.03</td>\n",
       "      <td>301.61</td>\n",
       "      <td>396.57</td>\n",
       "      <td>544.64</td>\n",
       "      <td>491.06</td>\n",
       "      <td>584.18</td>\n",
       "      <td>7452.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2069.31</td>\n",
       "      <td>1031.7</td>\n",
       "      <td>319.07</td>\n",
       "      <td>425.54</td>\n",
       "      <td>219.26</td>\n",
       "      <td>244.97</td>\n",
       "      <td>332.75</td>\n",
       "      <td>517.23</td>\n",
       "      <td>412.86</td>\n",
       "      <td>656.52</td>\n",
       "      <td>6229.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2369.82</td>\n",
       "      <td>1448.82</td>\n",
       "      <td>438.19</td>\n",
       "      <td>364.48</td>\n",
       "      <td>271.62</td>\n",
       "      <td>705.41</td>\n",
       "      <td>386.61</td>\n",
       "      <td>500.41</td>\n",
       "      <td>429.84</td>\n",
       "      <td>630.85</td>\n",
       "      <td>7546.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2674.71</td>\n",
       "      <td>1942.93</td>\n",
       "      <td>561.18</td>\n",
       "      <td>548.78</td>\n",
       "      <td>352.01</td>\n",
       "      <td>712.86</td>\n",
       "      <td>430.09</td>\n",
       "      <td>540.55</td>\n",
       "      <td>473.76</td>\n",
       "      <td>832.34</td>\n",
       "      <td>9069.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2          广州       深圳      珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "年                                                                        \n",
       "2000  2299.96  1325.33  382.93  483.04  214.28  275.92  372.43  501.66   \n",
       "2001  2511.68  1455.57  428.98  491.06  247.03  301.61  396.57  544.64   \n",
       "2002  2069.31   1031.7  319.07  425.54  219.26  244.97  332.75  517.23   \n",
       "2003  2369.82  1448.82  438.19  364.48  271.62  705.41  386.61  500.41   \n",
       "2004  2674.71  1942.93  561.18  548.78  352.01  712.86  430.09  540.55   \n",
       "\n",
       "2         肇庆      澳门   粤港澳大湾区  \n",
       "年                              \n",
       "2000  389.14  519.67  6764.36  \n",
       "2001  491.06  584.18  7452.38  \n",
       "2002  412.86  656.52  6229.21  \n",
       "2003  429.84  630.85  7546.05  \n",
       "2004  473.76  832.34  9069.21  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.set_index('年', inplace=True)\n",
    "data5[\"粤港澳大湾区\"] = data5.sum(axis=1)\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5695e4f1",
   "metadata": {},
   "source": [
    "### x5 人均本地生产总值的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4cc26ae",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
       "      <td>NaN</td>\n",
       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区\\n(港元)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门\\n(澳门元)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>23116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12876</td>\n",
       "      <td>...</td>\n",
       "      <td>13828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122431</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>25758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13877</td>\n",
       "      <td>...</td>\n",
       "      <td>15077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126271</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>28700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14590</td>\n",
       "      <td>...</td>\n",
       "      <td>17035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>32544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15529</td>\n",
       "      <td>...</td>\n",
       "      <td>19636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135079</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>38621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16860</td>\n",
       "      <td>...</td>\n",
       "      <td>23731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149113</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>46182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19189</td>\n",
       "      <td>...</td>\n",
       "      <td>29310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187793</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>54160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21942</td>\n",
       "      <td>...</td>\n",
       "      <td>36800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205753</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3  Unnamed: 4 Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市人均本地生产总值        NaN        NaN        NaN         NaN        NaN   \n",
       "1               NaN        NaN        NaN        NaN         NaN        NaN   \n",
       "2                 年         广州        NaN         深圳         NaN         珠海   \n",
       "3              1999      23116        NaN      30088         NaN      25568   \n",
       "4              2000      25758        NaN      33276         NaN      28068   \n",
       "5              2001      28700        NaN      35389         NaN      29590   \n",
       "6              2002      32544        NaN      41017         NaN      31920   \n",
       "7              2003      38621        NaN      47743         NaN      36258   \n",
       "8              2004      46182        NaN      55099         NaN      40776   \n",
       "9              2005      54160        NaN      61843         NaN      45682   \n",
       "\n",
       "   Unnamed: 6 Unnamed: 7  Unnamed: 8 Unnamed: 9  ...  Unnamed: 13 Unnamed: 14  \\\n",
       "0         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "1         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "2         NaN         佛山         NaN         惠州  ...           中山         NaN   \n",
       "3         NaN      18446         NaN      12876  ...        13828         NaN   \n",
       "4         NaN      20231         NaN      13877  ...        15077         NaN   \n",
       "5         NaN      21958         NaN      14590  ...        17035         NaN   \n",
       "6         NaN      24030         NaN      15529  ...        19636         NaN   \n",
       "7         NaN      28162         NaN      16860  ...        23731         NaN   \n",
       "8         NaN      33997         NaN      19189  ...        29310         NaN   \n",
       "9         NaN      42434         NaN      21942  ...        36800         NaN   \n",
       "\n",
       "   Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19 Unnamed: 20  \\\n",
       "0          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "1          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "2           江门         NaN           肇庆         NaN   香港特区\\n(港元)         NaN   \n",
       "3        11976         NaN         7138         NaN       194649         NaN   \n",
       "4        12844         NaN         7422         NaN       200675         NaN   \n",
       "5        13392         NaN         7827         NaN       196765         NaN   \n",
       "6        14022         NaN         8401         NaN       192367         NaN   \n",
       "7        15235         NaN         9258         NaN       186704         NaN   \n",
       "8        17039         NaN        10845         NaN       194140         NaN   \n",
       "9        19546         NaN        11505         NaN       207263         NaN   \n",
       "\n",
       "   Unnamed: 21 Unnamed: 22  \n",
       "0          NaN         NaN  \n",
       "1         元人民币         NaN  \n",
       "2    澳门\\n(澳门元)         NaN  \n",
       "3       122431         NaN  \n",
       "4       126271         NaN  \n",
       "5       127015         NaN  \n",
       "6       135079         NaN  \n",
       "7       149113         NaN  \n",
       "8       187793         NaN  \n",
       "9       205753         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data6 = pd.read_excel(r\"粤港澳大湾区城市人均本地生产总值.xls\")\n",
    "data6.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "927962f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区\\n(港元)</th>\n",
       "      <th>澳门\\n(澳门元)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>23116</td>\n",
       "      <td>30088</td>\n",
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       "      <td>12876</td>\n",
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       "      <th>1</th>\n",
       "      <td>2000</td>\n",
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       "      <td>33276</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>28700</td>\n",
       "      <td>35389</td>\n",
       "      <td>29590</td>\n",
       "      <td>21958</td>\n",
       "      <td>14590</td>\n",
       "      <td>15293</td>\n",
       "      <td>17035</td>\n",
       "      <td>13392</td>\n",
       "      <td>7827</td>\n",
       "      <td>196765</td>\n",
       "      <td>127015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>32544</td>\n",
       "      <td>41017</td>\n",
       "      <td>31920</td>\n",
       "      <td>24030</td>\n",
       "      <td>15529</td>\n",
       "      <td>18164</td>\n",
       "      <td>19636</td>\n",
       "      <td>14022</td>\n",
       "      <td>8401</td>\n",
       "      <td>192367</td>\n",
       "      <td>135079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>38621</td>\n",
       "      <td>47743</td>\n",
       "      <td>36258</td>\n",
       "      <td>28162</td>\n",
       "      <td>16860</td>\n",
       "      <td>22208</td>\n",
       "      <td>23731</td>\n",
       "      <td>15235</td>\n",
       "      <td>9258</td>\n",
       "      <td>186704</td>\n",
       "      <td>149113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年     广州     深圳     珠海     佛山     惠州     东莞     中山     江门    肇庆  \\\n",
       "0  1999  23116  30088  25568  18446  12876  12566  13828  11976  7138   \n",
       "1  2000  25758  33276  28068  20231  13877  13563  15077  12844  7422   \n",
       "2  2001  28700  35389  29590  21958  14590  15293  17035  13392  7827   \n",
       "3  2002  32544  41017  31920  24030  15529  18164  19636  14022  8401   \n",
       "4  2003  38621  47743  36258  28162  16860  22208  23731  15235  9258   \n",
       "\n",
       "2 香港特区\\n(港元) 澳门\\n(澳门元)  \n",
       "0     194649    122431  \n",
       "1     200675    126271  \n",
       "2     196765    127015  \n",
       "3     192367    135079  \n",
       "4     186704    149113  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data6.iloc[2]\n",
    "data6 = data6[3:]\n",
    "data6.columns = new_header\n",
    "data6 = data6.reset_index(drop=True)\n",
    "data6 = data6.dropna(thresh=2)\n",
    "data6 = data6.dropna(thresh=3, axis=1)\n",
    "data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ba8a3ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
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       "      <th>0</th>\n",
       "      <td>1999</td>\n",
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       "      <td>12876</td>\n",
       "      <td>12566</td>\n",
       "      <td>13828</td>\n",
       "      <td>11976</td>\n",
       "      <td>7138</td>\n",
       "      <td>179077.08</td>\n",
       "      <td>110187.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>25758</td>\n",
       "      <td>33276</td>\n",
       "      <td>28068</td>\n",
       "      <td>20231</td>\n",
       "      <td>13877</td>\n",
       "      <td>13563</td>\n",
       "      <td>15077</td>\n",
       "      <td>12844</td>\n",
       "      <td>7422</td>\n",
       "      <td>184621.00</td>\n",
       "      <td>113643.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>28700</td>\n",
       "      <td>35389</td>\n",
       "      <td>29590</td>\n",
       "      <td>21958</td>\n",
       "      <td>14590</td>\n",
       "      <td>15293</td>\n",
       "      <td>17035</td>\n",
       "      <td>13392</td>\n",
       "      <td>7827</td>\n",
       "      <td>181023.80</td>\n",
       "      <td>114313.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>32544</td>\n",
       "      <td>41017</td>\n",
       "      <td>31920</td>\n",
       "      <td>24030</td>\n",
       "      <td>15529</td>\n",
       "      <td>18164</td>\n",
       "      <td>19636</td>\n",
       "      <td>14022</td>\n",
       "      <td>8401</td>\n",
       "      <td>176977.64</td>\n",
       "      <td>121571.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>38621</td>\n",
       "      <td>47743</td>\n",
       "      <td>36258</td>\n",
       "      <td>28162</td>\n",
       "      <td>16860</td>\n",
       "      <td>22208</td>\n",
       "      <td>23731</td>\n",
       "      <td>15235</td>\n",
       "      <td>9258</td>\n",
       "      <td>171767.68</td>\n",
       "      <td>134201.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年     广州     深圳     珠海     佛山     惠州     东莞     中山     江门    肇庆  \\\n",
       "0  1999  23116  30088  25568  18446  12876  12566  13828  11976  7138   \n",
       "1  2000  25758  33276  28068  20231  13877  13563  15077  12844  7422   \n",
       "2  2001  28700  35389  29590  21958  14590  15293  17035  13392  7827   \n",
       "3  2002  32544  41017  31920  24030  15529  18164  19636  14022  8401   \n",
       "4  2003  38621  47743  36258  28162  16860  22208  23731  15235  9258   \n",
       "\n",
       "2       香港特区        澳门  \n",
       "0  179077.08  110187.9  \n",
       "1  184621.00  113643.9  \n",
       "2  181023.80  114313.5  \n",
       "3  176977.64  121571.1  \n",
       "4  171767.68  134201.7  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 汇率\n",
    "HKD_TO_CNY = 0.92\n",
    "MOP_TO_CNY = 0.90\n",
    "\n",
    "# 将香港特区（亿港元）列和澳门（亿澳门元）列的数据转换为人民币\n",
    "data6['香港特区'] = data6['香港特区\\n(港元)'].astype(float) * HKD_TO_CNY\n",
    "data6['澳门'] = data6['澳门\\n(澳门元)'].astype(float) * MOP_TO_CNY\n",
    "\n",
    "# 删除原有的港元和澳门元列\n",
    "data6.drop(columns=['香港特区\\n(港元)', '澳门\\n(澳门元)'], inplace=True)\n",
    "data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b67e5cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
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       "      <td>12844</td>\n",
       "      <td>7422</td>\n",
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       "      <td>2001</td>\n",
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       "      <td>35389</td>\n",
       "      <td>29590</td>\n",
       "      <td>21958</td>\n",
       "      <td>14590</td>\n",
       "      <td>15293</td>\n",
       "      <td>17035</td>\n",
       "      <td>13392</td>\n",
       "      <td>7827</td>\n",
       "      <td>181023.80</td>\n",
       "      <td>114313.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>32544</td>\n",
       "      <td>41017</td>\n",
       "      <td>31920</td>\n",
       "      <td>24030</td>\n",
       "      <td>15529</td>\n",
       "      <td>18164</td>\n",
       "      <td>19636</td>\n",
       "      <td>14022</td>\n",
       "      <td>8401</td>\n",
       "      <td>176977.64</td>\n",
       "      <td>121571.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>38621</td>\n",
       "      <td>47743</td>\n",
       "      <td>36258</td>\n",
       "      <td>28162</td>\n",
       "      <td>16860</td>\n",
       "      <td>22208</td>\n",
       "      <td>23731</td>\n",
       "      <td>15235</td>\n",
       "      <td>9258</td>\n",
       "      <td>171767.68</td>\n",
       "      <td>134201.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年     广州     深圳     珠海     佛山     惠州     东莞     中山     江门    肇庆  \\\n",
       "0  1999  23116  30088  25568  18446  12876  12566  13828  11976  7138   \n",
       "1  2000  25758  33276  28068  20231  13877  13563  15077  12844  7422   \n",
       "2  2001  28700  35389  29590  21958  14590  15293  17035  13392  7827   \n",
       "3  2002  32544  41017  31920  24030  15529  18164  19636  14022  8401   \n",
       "4  2003  38621  47743  36258  28162  16860  22208  23731  15235  9258   \n",
       "\n",
       "2       香港特区        澳门  \n",
       "0  179077.08  110187.9  \n",
       "1  184621.00  113643.9  \n",
       "2  181023.80  114313.5  \n",
       "3  176977.64  121571.1  \n",
       "4  171767.68  134201.7  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6 = data6[:-1]\n",
    "data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac4a0f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
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       "      <th>1999</th>\n",
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       "      <td>7138</td>\n",
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       "      <td>444866.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>25758</td>\n",
       "      <td>33276</td>\n",
       "      <td>28068</td>\n",
       "      <td>20231</td>\n",
       "      <td>13877</td>\n",
       "      <td>13563</td>\n",
       "      <td>15077</td>\n",
       "      <td>12844</td>\n",
       "      <td>7422</td>\n",
       "      <td>184621.00</td>\n",
       "      <td>113643.9</td>\n",
       "      <td>468380.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>28700</td>\n",
       "      <td>35389</td>\n",
       "      <td>29590</td>\n",
       "      <td>21958</td>\n",
       "      <td>14590</td>\n",
       "      <td>15293</td>\n",
       "      <td>17035</td>\n",
       "      <td>13392</td>\n",
       "      <td>7827</td>\n",
       "      <td>181023.80</td>\n",
       "      <td>114313.5</td>\n",
       "      <td>479111.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>32544</td>\n",
       "      <td>41017</td>\n",
       "      <td>31920</td>\n",
       "      <td>24030</td>\n",
       "      <td>15529</td>\n",
       "      <td>18164</td>\n",
       "      <td>19636</td>\n",
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       "      <td>8401</td>\n",
       "      <td>176977.64</td>\n",
       "      <td>121571.1</td>\n",
       "      <td>503811.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>38621</td>\n",
       "      <td>47743</td>\n",
       "      <td>36258</td>\n",
       "      <td>28162</td>\n",
       "      <td>16860</td>\n",
       "      <td>22208</td>\n",
       "      <td>23731</td>\n",
       "      <td>15235</td>\n",
       "      <td>9258</td>\n",
       "      <td>171767.68</td>\n",
       "      <td>134201.7</td>\n",
       "      <td>544045.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2        广州     深圳     珠海     佛山     惠州     东莞     中山     江门    肇庆       香港特区  \\\n",
       "年                                                                               \n",
       "1999  23116  30088  25568  18446  12876  12566  13828  11976  7138  179077.08   \n",
       "2000  25758  33276  28068  20231  13877  13563  15077  12844  7422  184621.00   \n",
       "2001  28700  35389  29590  21958  14590  15293  17035  13392  7827  181023.80   \n",
       "2002  32544  41017  31920  24030  15529  18164  19636  14022  8401  176977.64   \n",
       "2003  38621  47743  36258  28162  16860  22208  23731  15235  9258  171767.68   \n",
       "\n",
       "2           澳门     粤港澳大湾区  \n",
       "年                          \n",
       "1999  110187.9  444866.98  \n",
       "2000  113643.9   468380.9  \n",
       "2001  114313.5   479111.3  \n",
       "2002  121571.1  503811.74  \n",
       "2003  134201.7  544045.38  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6.set_index('年', inplace=True)\n",
    "data6[\"粤港澳大湾区\"] = data6.sum(axis=1)\n",
    "data6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec45105",
   "metadata": {},
   "source": [
    "### x6 城市人口的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0955f67c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>万人</td>\n",
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       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
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       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>珠海</td>\n",
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       "      <td>佛山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>惠州</td>\n",
       "      <td>...</td>\n",
       "      <td>中山</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江门</td>\n",
       "      <td>NaN</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香港特区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳门</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>950.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>504.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>311.19</td>\n",
       "      <td>...</td>\n",
       "      <td>221.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>390.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>663.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.96</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>994.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>701.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>534.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.8</td>\n",
       "      <td>...</td>\n",
       "      <td>236.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>395.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>671.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>996.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>724.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>549.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.77</td>\n",
       "      <td>...</td>\n",
       "      <td>238.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>402.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>673.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002</td>\n",
       "      <td>984.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>746.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>556.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>343.41</td>\n",
       "      <td>...</td>\n",
       "      <td>240.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>404.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>352.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003</td>\n",
       "      <td>972.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>778.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>564.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>352.27</td>\n",
       "      <td>...</td>\n",
       "      <td>241.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>406.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>676.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.67</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2004</td>\n",
       "      <td>966.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363.18</td>\n",
       "      <td>...</td>\n",
       "      <td>242.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>410.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>679.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>949.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>827.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>580.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.69</td>\n",
       "      <td>...</td>\n",
       "      <td>243.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>410.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>367.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>683.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Unnamed: 1  Unnamed: 2 Unnamed: 3  Unnamed: 4 Unnamed: 5  \\\n",
       "0  粤港澳大湾区城市人口        NaN         NaN        NaN         NaN        NaN   \n",
       "1         NaN        NaN         NaN        NaN         NaN        NaN   \n",
       "2           年         广州         NaN         深圳         NaN         珠海   \n",
       "3        1999     950.65         NaN     632.56         NaN     115.71   \n",
       "4        2000      994.8         NaN     701.24         NaN     123.65   \n",
       "5        2001     996.75         NaN     724.57         NaN     128.45   \n",
       "6        2002     984.76         NaN     746.62         NaN     131.61   \n",
       "7        2003     972.93         NaN     778.27         NaN     134.85   \n",
       "8        2004     966.06         NaN      800.8         NaN     138.86   \n",
       "9        2005     949.68         NaN     827.75         NaN     141.57   \n",
       "\n",
       "   Unnamed: 6 Unnamed: 7  Unnamed: 8 Unnamed: 9  ...  Unnamed: 13 Unnamed: 14  \\\n",
       "0         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "1         NaN        NaN         NaN        NaN  ...          NaN         NaN   \n",
       "2         NaN         佛山         NaN         惠州  ...           中山         NaN   \n",
       "3         NaN     504.34         NaN     311.19  ...       221.75         NaN   \n",
       "4         NaN     534.05         NaN      321.8  ...       236.47         NaN   \n",
       "5         NaN     549.09         NaN     334.77  ...        238.3         NaN   \n",
       "6         NaN     556.66         NaN     343.41  ...       240.13         NaN   \n",
       "7         NaN     564.35         NaN     352.27  ...       241.98         NaN   \n",
       "8         NaN     575.01         NaN     363.18  ...       242.73         NaN   \n",
       "9         NaN     580.03         NaN     370.69  ...       243.46         NaN   \n",
       "\n",
       "   Unnamed: 15 Unnamed: 16  Unnamed: 17 Unnamed: 18  Unnamed: 19 Unnamed: 20  \\\n",
       "0          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "1          NaN         NaN          NaN         NaN          NaN         NaN   \n",
       "2           江门         NaN           肇庆         NaN         香港特区         NaN   \n",
       "3       390.16         NaN        335.4         NaN       663.76         NaN   \n",
       "4       395.24         NaN       337.69         NaN       671.15         NaN   \n",
       "5        402.7         NaN       347.04         NaN       673.03         NaN   \n",
       "6       404.58         NaN       352.07         NaN       672.58         NaN   \n",
       "7       406.48         NaN       357.17         NaN       676.42         NaN   \n",
       "8       410.03         NaN       364.17         NaN       679.77         NaN   \n",
       "9       410.29         NaN        367.6         NaN       683.78         NaN   \n",
       "\n",
       "   Unnamed: 21 Unnamed: 22  \n",
       "0          NaN         NaN  \n",
       "1          NaN          万人  \n",
       "2           澳门         NaN  \n",
       "3        42.96         NaN  \n",
       "4        43.15         NaN  \n",
       "5        43.63         NaN  \n",
       "6        44.05         NaN  \n",
       "7        44.67         NaN  \n",
       "8        46.26         NaN  \n",
       "9        48.43         NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data7 = pd.read_excel(r\"粤港澳大湾区城市人口.xls\")\n",
    "data7.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "85fd47f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>950.65</td>\n",
       "      <td>632.56</td>\n",
       "      <td>115.71</td>\n",
       "      <td>504.34</td>\n",
       "      <td>311.19</td>\n",
       "      <td>565.98</td>\n",
       "      <td>221.75</td>\n",
       "      <td>390.16</td>\n",
       "      <td>335.4</td>\n",
       "      <td>663.76</td>\n",
       "      <td>42.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>994.8</td>\n",
       "      <td>701.24</td>\n",
       "      <td>123.65</td>\n",
       "      <td>534.05</td>\n",
       "      <td>321.8</td>\n",
       "      <td>644.84</td>\n",
       "      <td>236.47</td>\n",
       "      <td>395.24</td>\n",
       "      <td>337.69</td>\n",
       "      <td>671.15</td>\n",
       "      <td>43.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>996.75</td>\n",
       "      <td>724.57</td>\n",
       "      <td>128.45</td>\n",
       "      <td>549.09</td>\n",
       "      <td>334.77</td>\n",
       "      <td>654.43</td>\n",
       "      <td>238.3</td>\n",
       "      <td>402.7</td>\n",
       "      <td>347.04</td>\n",
       "      <td>673.03</td>\n",
       "      <td>43.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>984.76</td>\n",
       "      <td>746.62</td>\n",
       "      <td>131.61</td>\n",
       "      <td>556.66</td>\n",
       "      <td>343.41</td>\n",
       "      <td>654.84</td>\n",
       "      <td>240.13</td>\n",
       "      <td>404.58</td>\n",
       "      <td>352.07</td>\n",
       "      <td>672.58</td>\n",
       "      <td>44.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>972.93</td>\n",
       "      <td>778.27</td>\n",
       "      <td>134.85</td>\n",
       "      <td>564.35</td>\n",
       "      <td>352.27</td>\n",
       "      <td>655.25</td>\n",
       "      <td>241.98</td>\n",
       "      <td>406.48</td>\n",
       "      <td>357.17</td>\n",
       "      <td>676.42</td>\n",
       "      <td>44.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年      广州      深圳      珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "0  1999  950.65  632.56  115.71  504.34  311.19  565.98  221.75  390.16   \n",
       "1  2000   994.8  701.24  123.65  534.05   321.8  644.84  236.47  395.24   \n",
       "2  2001  996.75  724.57  128.45  549.09  334.77  654.43   238.3   402.7   \n",
       "3  2002  984.76  746.62  131.61  556.66  343.41  654.84  240.13  404.58   \n",
       "4  2003  972.93  778.27  134.85  564.35  352.27  655.25  241.98  406.48   \n",
       "\n",
       "2      肇庆    香港特区     澳门  \n",
       "0   335.4  663.76  42.96  \n",
       "1  337.69  671.15  43.15  \n",
       "2  347.04  673.03  43.63  \n",
       "3  352.07  672.58  44.05  \n",
       "4  357.17  676.42  44.67  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_header = data7.iloc[2]\n",
    "data7 = data7[3:]\n",
    "data7.columns = new_header\n",
    "data7 = data7.reset_index(drop=True)\n",
    "data7 = data7.dropna(thresh=2)\n",
    "data7 = data7.dropna(thresh=3, axis=1)\n",
    "data7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5abb7de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>年</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>950.65</td>\n",
       "      <td>632.56</td>\n",
       "      <td>115.71</td>\n",
       "      <td>504.34</td>\n",
       "      <td>311.19</td>\n",
       "      <td>565.98</td>\n",
       "      <td>221.75</td>\n",
       "      <td>390.16</td>\n",
       "      <td>335.4</td>\n",
       "      <td>663.76</td>\n",
       "      <td>42.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>994.8</td>\n",
       "      <td>701.24</td>\n",
       "      <td>123.65</td>\n",
       "      <td>534.05</td>\n",
       "      <td>321.8</td>\n",
       "      <td>644.84</td>\n",
       "      <td>236.47</td>\n",
       "      <td>395.24</td>\n",
       "      <td>337.69</td>\n",
       "      <td>671.15</td>\n",
       "      <td>43.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>996.75</td>\n",
       "      <td>724.57</td>\n",
       "      <td>128.45</td>\n",
       "      <td>549.09</td>\n",
       "      <td>334.77</td>\n",
       "      <td>654.43</td>\n",
       "      <td>238.3</td>\n",
       "      <td>402.7</td>\n",
       "      <td>347.04</td>\n",
       "      <td>673.03</td>\n",
       "      <td>43.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>984.76</td>\n",
       "      <td>746.62</td>\n",
       "      <td>131.61</td>\n",
       "      <td>556.66</td>\n",
       "      <td>343.41</td>\n",
       "      <td>654.84</td>\n",
       "      <td>240.13</td>\n",
       "      <td>404.58</td>\n",
       "      <td>352.07</td>\n",
       "      <td>672.58</td>\n",
       "      <td>44.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>972.93</td>\n",
       "      <td>778.27</td>\n",
       "      <td>134.85</td>\n",
       "      <td>564.35</td>\n",
       "      <td>352.27</td>\n",
       "      <td>655.25</td>\n",
       "      <td>241.98</td>\n",
       "      <td>406.48</td>\n",
       "      <td>357.17</td>\n",
       "      <td>676.42</td>\n",
       "      <td>44.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     年      广州      深圳      珠海      佛山      惠州      东莞      中山      江门  \\\n",
       "0  1999  950.65  632.56  115.71  504.34  311.19  565.98  221.75  390.16   \n",
       "1  2000   994.8  701.24  123.65  534.05   321.8  644.84  236.47  395.24   \n",
       "2  2001  996.75  724.57  128.45  549.09  334.77  654.43   238.3   402.7   \n",
       "3  2002  984.76  746.62  131.61  556.66  343.41  654.84  240.13  404.58   \n",
       "4  2003  972.93  778.27  134.85  564.35  352.27  655.25  241.98  406.48   \n",
       "\n",
       "2      肇庆    香港特区     澳门  \n",
       "0   335.4  663.76  42.96  \n",
       "1  337.69  671.15  43.15  \n",
       "2  347.04  673.03  43.63  \n",
       "3  352.07  672.58  44.05  \n",
       "4  357.17  676.42  44.67  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7 = data7[:-1]\n",
    "data7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c16155c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>香港特区</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳大湾区</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>950.65</td>\n",
       "      <td>632.56</td>\n",
       "      <td>115.71</td>\n",
       "      <td>504.34</td>\n",
       "      <td>311.19</td>\n",
       "      <td>565.98</td>\n",
       "      <td>221.75</td>\n",
       "      <td>390.16</td>\n",
       "      <td>335.4</td>\n",
       "      <td>663.76</td>\n",
       "      <td>42.96</td>\n",
       "      <td>4734.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>994.8</td>\n",
       "      <td>701.24</td>\n",
       "      <td>123.65</td>\n",
       "      <td>534.05</td>\n",
       "      <td>321.8</td>\n",
       "      <td>644.84</td>\n",
       "      <td>236.47</td>\n",
       "      <td>395.24</td>\n",
       "      <td>337.69</td>\n",
       "      <td>671.15</td>\n",
       "      <td>43.15</td>\n",
       "      <td>5004.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>996.75</td>\n",
       "      <td>724.57</td>\n",
       "      <td>128.45</td>\n",
       "      <td>549.09</td>\n",
       "      <td>334.77</td>\n",
       "      <td>654.43</td>\n",
       "      <td>238.3</td>\n",
       "      <td>402.7</td>\n",
       "      <td>347.04</td>\n",
       "      <td>673.03</td>\n",
       "      <td>43.63</td>\n",
       "      <td>5092.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>984.76</td>\n",
       "      <td>746.62</td>\n",
       "      <td>131.61</td>\n",
       "      <td>556.66</td>\n",
       "      <td>343.41</td>\n",
       "      <td>654.84</td>\n",
       "      <td>240.13</td>\n",
       "      <td>404.58</td>\n",
       "      <td>352.07</td>\n",
       "      <td>672.58</td>\n",
       "      <td>44.05</td>\n",
       "      <td>5131.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>972.93</td>\n",
       "      <td>778.27</td>\n",
       "      <td>134.85</td>\n",
       "      <td>564.35</td>\n",
       "      <td>352.27</td>\n",
       "      <td>655.25</td>\n",
       "      <td>241.98</td>\n",
       "      <td>406.48</td>\n",
       "      <td>357.17</td>\n",
       "      <td>676.42</td>\n",
       "      <td>44.67</td>\n",
       "      <td>5184.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2         广州      深圳      珠海      佛山      惠州      东莞      中山      江门      肇庆  \\\n",
       "年                                                                              \n",
       "1999  950.65  632.56  115.71  504.34  311.19  565.98  221.75  390.16   335.4   \n",
       "2000   994.8  701.24  123.65  534.05   321.8  644.84  236.47  395.24  337.69   \n",
       "2001  996.75  724.57  128.45  549.09  334.77  654.43   238.3   402.7  347.04   \n",
       "2002  984.76  746.62  131.61  556.66  343.41  654.84  240.13  404.58  352.07   \n",
       "2003  972.93  778.27  134.85  564.35  352.27  655.25  241.98  406.48  357.17   \n",
       "\n",
       "2       香港特区     澳门   粤港澳大湾区  \n",
       "年                             \n",
       "1999  663.76  42.96  4734.46  \n",
       "2000  671.15  43.15  5004.08  \n",
       "2001  673.03  43.63  5092.76  \n",
       "2002  672.58  44.05  5131.31  \n",
       "2003  676.42  44.67  5184.64  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7.set_index('年', inplace=True)\n",
    "data7[\"粤港澳大湾区\"] = data7.sum(axis=1)\n",
    "data7.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d140f7",
   "metadata": {},
   "source": [
    "### x7 博物馆数量的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a24d29d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数据来源：中经数据（CEIdata）</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
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       "      <th>Unnamed: 11</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>序列ID</td>\n",
       "      <td>4311147</td>\n",
       "      <td>4311158</td>\n",
       "      <td>4311159</td>\n",
       "      <td>4311166</td>\n",
       "      <td>4311168</td>\n",
       "      <td>4311176</td>\n",
       "      <td>4311184</td>\n",
       "      <td>4311205</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>指标</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "      <td>博物馆数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年</td>\n",
       "      <td>广州</td>\n",
       "      <td>深圳</td>\n",
       "      <td>珠海</td>\n",
       "      <td>佛山</td>\n",
       "      <td>江门</td>\n",
       "      <td>肇庆</td>\n",
       "      <td>惠州</td>\n",
       "      <td>中山</td>\n",
       "      <td>东莞</td>\n",
       "      <td>澳门</td>\n",
       "      <td>香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>频度</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "      <td>年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>单位</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "      <td>个</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  数据来源：中经数据（CEIdata） Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5  \\\n",
       "0               序列ID    4311147    4311158    4311159    4311166    4311168   \n",
       "1                 指标       博物馆数       博物馆数       博物馆数       博物馆数       博物馆数   \n",
       "2                  年         广州         深圳         珠海         佛山         江门   \n",
       "3                 频度          年          年          年          年          年   \n",
       "4                 单位          个          个          个          个          个   \n",
       "5               2011         28         16          3          9          9   \n",
       "6               2012         29         16          3          9          9   \n",
       "7               2013         30         26          3         16         10   \n",
       "8               2014         30         26          3         16         10   \n",
       "9               2015         30         26          7         16         10   \n",
       "\n",
       "  Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10 Unnamed: 11  \n",
       "0    4311176    4311184    4311205    4311204         NaN         NaN  \n",
       "1       博物馆数       博物馆数       博物馆数       博物馆数        博物馆数        博物馆数  \n",
       "2         肇庆         惠州         中山         东莞          澳门          香港  \n",
       "3          年          年          年          年           年           年  \n",
       "4          个          个          个          个           个           个  \n",
       "5          7          6          4          5          21          27  \n",
       "6          7          6          4          7          21          30  \n",
       "7          7          6          6          7          21          36  \n",
       "8          7          6          6          7          25          42  \n",
       "9          8          6          6          7          25          45  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data8 = pd.read_excel(r\"粤港澳_博物馆数_（文化）.xlsx\")\n",
    "data8.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b5b5a11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
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       "      <th>江门</th>\n",
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       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
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       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>28</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>61</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>64</td>\n",
       "      <td>86</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      广州  深圳  珠海  佛山  江门  肇庆  惠州  中山  东莞  澳门  香港\n",
       "年                                               \n",
       "2011  28  16   3   9   9   7   6   4   5  21  27\n",
       "2012  29  16   3   9   9   7   6   4   7  21  30\n",
       "2013  30  26   3  16  10   7   6   6   7  21  36\n",
       "2014  30  26   3  16  10   7   6   6   7  25  42\n",
       "2015  30  26   7  16  10   8   6   6   7  25  45\n",
       "2016  29  24   7  17  10   8   6   6   7  25  47\n",
       "2017  29  20   7  17  11   8   7   6   7  25  50\n",
       "2018  29  19   7  17  12   9   7   6   7  25  50\n",
       "2019  28  55   8  21  13   8   9   9  16  31  53\n",
       "2020  61  63   9  25  15  10  10   9  18  31  55\n",
       "2021  64  86  10  27  12  17  10   9  20  31  59\n",
       "2022  59  93  11  29  15  16  10   6  20  31  60\n",
       "2023  59  93  11  30  15  16  10   6  20  35  60"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data8 = pd.read_excel(r\"粤港澳_博物馆数_（文化）.xlsx\",skiprows=3,index_col=0)\n",
    "data8 = data8.drop([data8.index[0], data8.index[1]])\n",
    "data8 = data8.fillna(method='ffill')\n",
    "data8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1f256675",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>中山</th>\n",
       "      <th>东莞</th>\n",
       "      <th>澳门</th>\n",
       "      <th>香港</th>\n",
       "      <th>粤港澳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>28</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>53</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>61</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>55</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>64</td>\n",
       "      <td>86</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>59</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>60</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      广州  深圳  珠海  佛山  江门  肇庆  惠州  中山  东莞  澳门  香港  粤港澳\n",
       "年                                                    \n",
       "2011  28  16   3   9   9   7   6   4   5  21  27  135\n",
       "2012  29  16   3   9   9   7   6   4   7  21  30  141\n",
       "2013  30  26   3  16  10   7   6   6   7  21  36  168\n",
       "2014  30  26   3  16  10   7   6   6   7  25  42  178\n",
       "2015  30  26   7  16  10   8   6   6   7  25  45  186\n",
       "2016  29  24   7  17  10   8   6   6   7  25  47  186\n",
       "2017  29  20   7  17  11   8   7   6   7  25  50  187\n",
       "2018  29  19   7  17  12   9   7   6   7  25  50  188\n",
       "2019  28  55   8  21  13   8   9   9  16  31  53  251\n",
       "2020  61  63   9  25  15  10  10   9  18  31  55  306\n",
       "2021  64  86  10  27  12  17  10   9  20  31  59  345\n",
       "2022  59  93  11  29  15  16  10   6  20  31  60  350\n",
       "2023  59  93  11  30  15  16  10   6  20  35  60  355"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data8['粤港澳']=data8.sum(axis=1)\n",
    "data8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1ef2f6",
   "metadata": {},
   "source": [
    "### x8 工业增加值占GDP比重（产业）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd43542b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>香港</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>34.79</td>\n",
       "      <td>44.1</td>\n",
       "      <td>51.25</td>\n",
       "      <td>60.89</td>\n",
       "      <td>53.06</td>\n",
       "      <td>31</td>\n",
       "      <td>55.7</td>\n",
       "      <td>48.95</td>\n",
       "      <td>55.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>34.5</td>\n",
       "      <td>44.1</td>\n",
       "      <td>50.84</td>\n",
       "      <td>60.69</td>\n",
       "      <td>52.07</td>\n",
       "      <td>33.6</td>\n",
       "      <td>54.84</td>\n",
       "      <td>48.33</td>\n",
       "      <td>53.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>32.88</td>\n",
       "      <td>41.9</td>\n",
       "      <td>47.9</td>\n",
       "      <td>60.61</td>\n",
       "      <td>48.6</td>\n",
       "      <td>35.7</td>\n",
       "      <td>54.76</td>\n",
       "      <td>45.86</td>\n",
       "      <td>52.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>32.03</td>\n",
       "      <td>40.7</td>\n",
       "      <td>46.65</td>\n",
       "      <td>59.6</td>\n",
       "      <td>48.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>54.69</td>\n",
       "      <td>44.37</td>\n",
       "      <td>53.21</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>31.74</td>\n",
       "      <td>40.2</td>\n",
       "      <td>45.16</td>\n",
       "      <td>60.76</td>\n",
       "      <td>46.35</td>\n",
       "      <td>41</td>\n",
       "      <td>53.4</td>\n",
       "      <td>46.06</td>\n",
       "      <td>53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>30.17</td>\n",
       "      <td>39</td>\n",
       "      <td>44.14</td>\n",
       "      <td>59.27</td>\n",
       "      <td>45.67</td>\n",
       "      <td>41.4</td>\n",
       "      <td>51.73</td>\n",
       "      <td>45.26</td>\n",
       "      <td>52.06</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>28.4</td>\n",
       "      <td>37.6</td>\n",
       "      <td>43.16</td>\n",
       "      <td>58.14</td>\n",
       "      <td>44.86</td>\n",
       "      <td>39.5</td>\n",
       "      <td>50.81</td>\n",
       "      <td>45.25</td>\n",
       "      <td>50.21</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>26.88</td>\n",
       "      <td>37.4</td>\n",
       "      <td>42.36</td>\n",
       "      <td>55.63</td>\n",
       "      <td>46.59</td>\n",
       "      <td>37.5</td>\n",
       "      <td>49.55</td>\n",
       "      <td>47.06</td>\n",
       "      <td>48.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>26.13</td>\n",
       "      <td>36.4</td>\n",
       "      <td>43.14</td>\n",
       "      <td>54.03</td>\n",
       "      <td>45.85</td>\n",
       "      <td>35.9</td>\n",
       "      <td>49.55</td>\n",
       "      <td>47.24</td>\n",
       "      <td>46.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>23.96</td>\n",
       "      <td>35.3</td>\n",
       "      <td>38.2</td>\n",
       "      <td>53.97</td>\n",
       "      <td>38.2</td>\n",
       "      <td>34.35</td>\n",
       "      <td>47.7</td>\n",
       "      <td>54.7</td>\n",
       "      <td>46.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>23.3</td>\n",
       "      <td>34.3</td>\n",
       "      <td>36.67</td>\n",
       "      <td>51.67</td>\n",
       "      <td>36.49</td>\n",
       "      <td>33.38</td>\n",
       "      <td>45.8</td>\n",
       "      <td>51.55</td>\n",
       "      <td>46.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>24.01</td>\n",
       "      <td>34.5</td>\n",
       "      <td>36.76</td>\n",
       "      <td>52.59</td>\n",
       "      <td>39.89</td>\n",
       "      <td>36.15</td>\n",
       "      <td>48</td>\n",
       "      <td>55.99</td>\n",
       "      <td>46.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>24.09</td>\n",
       "      <td>34.6</td>\n",
       "      <td>39.03</td>\n",
       "      <td>53.09</td>\n",
       "      <td>39.33</td>\n",
       "      <td>36.66</td>\n",
       "      <td>50.32</td>\n",
       "      <td>55.82</td>\n",
       "      <td>46.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         广州    深圳     珠海     佛山     江门     肇庆     惠州     东莞     中山   香港   澳门\n",
       "年                                                                           \n",
       "2010  34.79  44.1  51.25  60.89  53.06     31   55.7  48.95  55.22  NaN  NaN\n",
       "2011   34.5  44.1  50.84  60.69  52.07   33.6  54.84  48.33   53.1  6.7  4.2\n",
       "2012  32.88  41.9   47.9  60.61   48.6   35.7  54.76  45.86   52.9  6.8  4.0\n",
       "2013  32.03  40.7  46.65   59.6   48.2   40.2  54.69  44.37  53.21  6.9  3.7\n",
       "2014  31.74  40.2  45.16  60.76  46.35     41   53.4  46.06     53  7.1  5.0\n",
       "2015  30.17    39  44.14  59.27  45.67   41.4  51.73  45.26  52.06  7.1  7.7\n",
       "2016   28.4  37.6  43.16  58.14  44.86   39.5  50.81  45.25  50.21  7.5  6.6\n",
       "2017  26.88  37.4  42.36  55.63  46.59   37.5  49.55  47.06  48.05  NaN  NaN\n",
       "2018  26.13  36.4  43.14  54.03  45.85   35.9  49.55  47.24  46.67  NaN  NaN\n",
       "2019  23.96  35.3   38.2  53.97   38.2  34.35   47.7   54.7   46.4  NaN  NaN\n",
       "2020   23.3  34.3  36.67  51.67  36.49  33.38   45.8  51.55  46.23  NaN  NaN\n",
       "2021  24.01  34.5  36.76  52.59  39.89  36.15     48  55.99  46.24  NaN  NaN\n",
       "2022  24.09  34.6  39.03  53.09  39.33  36.66  50.32  55.82   46.2  NaN  NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data9 = pd.read_excel(r\"粤港澳大湾区工业增加值占GDP比重.xlsx\",skiprows=3,index_col=0)\n",
    "data9 = data9.drop([data9.index[0], data9.index[1]])\n",
    "data9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f15e809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>香港</th>\n",
       "      <th>澳门</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>34.79</td>\n",
       "      <td>44.1</td>\n",
       "      <td>51.25</td>\n",
       "      <td>60.89</td>\n",
       "      <td>53.06</td>\n",
       "      <td>31.00</td>\n",
       "      <td>55.70</td>\n",
       "      <td>48.95</td>\n",
       "      <td>55.22</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>34.50</td>\n",
       "      <td>44.1</td>\n",
       "      <td>50.84</td>\n",
       "      <td>60.69</td>\n",
       "      <td>52.07</td>\n",
       "      <td>33.60</td>\n",
       "      <td>54.84</td>\n",
       "      <td>48.33</td>\n",
       "      <td>53.10</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>32.88</td>\n",
       "      <td>41.9</td>\n",
       "      <td>47.90</td>\n",
       "      <td>60.61</td>\n",
       "      <td>48.60</td>\n",
       "      <td>35.70</td>\n",
       "      <td>54.76</td>\n",
       "      <td>45.86</td>\n",
       "      <td>52.90</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>32.03</td>\n",
       "      <td>40.7</td>\n",
       "      <td>46.65</td>\n",
       "      <td>59.60</td>\n",
       "      <td>48.20</td>\n",
       "      <td>40.20</td>\n",
       "      <td>54.69</td>\n",
       "      <td>44.37</td>\n",
       "      <td>53.21</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>31.74</td>\n",
       "      <td>40.2</td>\n",
       "      <td>45.16</td>\n",
       "      <td>60.76</td>\n",
       "      <td>46.35</td>\n",
       "      <td>41.00</td>\n",
       "      <td>53.40</td>\n",
       "      <td>46.06</td>\n",
       "      <td>53.00</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>30.17</td>\n",
       "      <td>39.0</td>\n",
       "      <td>44.14</td>\n",
       "      <td>59.27</td>\n",
       "      <td>45.67</td>\n",
       "      <td>41.40</td>\n",
       "      <td>51.73</td>\n",
       "      <td>45.26</td>\n",
       "      <td>52.06</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>28.40</td>\n",
       "      <td>37.6</td>\n",
       "      <td>43.16</td>\n",
       "      <td>58.14</td>\n",
       "      <td>44.86</td>\n",
       "      <td>39.50</td>\n",
       "      <td>50.81</td>\n",
       "      <td>45.25</td>\n",
       "      <td>50.21</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>26.88</td>\n",
       "      <td>37.4</td>\n",
       "      <td>42.36</td>\n",
       "      <td>55.63</td>\n",
       "      <td>46.59</td>\n",
       "      <td>37.50</td>\n",
       "      <td>49.55</td>\n",
       "      <td>47.06</td>\n",
       "      <td>48.05</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>26.13</td>\n",
       "      <td>36.4</td>\n",
       "      <td>43.14</td>\n",
       "      <td>54.03</td>\n",
       "      <td>45.85</td>\n",
       "      <td>35.90</td>\n",
       "      <td>49.55</td>\n",
       "      <td>47.24</td>\n",
       "      <td>46.67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>23.96</td>\n",
       "      <td>35.3</td>\n",
       "      <td>38.20</td>\n",
       "      <td>53.97</td>\n",
       "      <td>38.20</td>\n",
       "      <td>34.35</td>\n",
       "      <td>47.70</td>\n",
       "      <td>54.70</td>\n",
       "      <td>46.40</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>23.30</td>\n",
       "      <td>34.3</td>\n",
       "      <td>36.67</td>\n",
       "      <td>51.67</td>\n",
       "      <td>36.49</td>\n",
       "      <td>33.38</td>\n",
       "      <td>45.80</td>\n",
       "      <td>51.55</td>\n",
       "      <td>46.23</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>24.01</td>\n",
       "      <td>34.5</td>\n",
       "      <td>36.76</td>\n",
       "      <td>52.59</td>\n",
       "      <td>39.89</td>\n",
       "      <td>36.15</td>\n",
       "      <td>48.00</td>\n",
       "      <td>55.99</td>\n",
       "      <td>46.24</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>24.09</td>\n",
       "      <td>34.6</td>\n",
       "      <td>39.03</td>\n",
       "      <td>53.09</td>\n",
       "      <td>39.33</td>\n",
       "      <td>36.66</td>\n",
       "      <td>50.32</td>\n",
       "      <td>55.82</td>\n",
       "      <td>46.20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         广州    深圳     珠海     佛山     江门     肇庆     惠州     东莞     中山   香港   澳门\n",
       "年                                                                           \n",
       "2010  34.79  44.1  51.25  60.89  53.06  31.00  55.70  48.95  55.22  6.7  4.2\n",
       "2011  34.50  44.1  50.84  60.69  52.07  33.60  54.84  48.33  53.10  6.7  4.2\n",
       "2012  32.88  41.9  47.90  60.61  48.60  35.70  54.76  45.86  52.90  6.8  4.0\n",
       "2013  32.03  40.7  46.65  59.60  48.20  40.20  54.69  44.37  53.21  6.9  3.7\n",
       "2014  31.74  40.2  45.16  60.76  46.35  41.00  53.40  46.06  53.00  7.1  5.0\n",
       "2015  30.17  39.0  44.14  59.27  45.67  41.40  51.73  45.26  52.06  7.1  7.7\n",
       "2016  28.40  37.6  43.16  58.14  44.86  39.50  50.81  45.25  50.21  7.5  6.6\n",
       "2017  26.88  37.4  42.36  55.63  46.59  37.50  49.55  47.06  48.05  7.5  6.6\n",
       "2018  26.13  36.4  43.14  54.03  45.85  35.90  49.55  47.24  46.67  7.5  6.6\n",
       "2019  23.96  35.3  38.20  53.97  38.20  34.35  47.70  54.70  46.40  7.5  6.6\n",
       "2020  23.30  34.3  36.67  51.67  36.49  33.38  45.80  51.55  46.23  7.5  6.6\n",
       "2021  24.01  34.5  36.76  52.59  39.89  36.15  48.00  55.99  46.24  7.5  6.6\n",
       "2022  24.09  34.6  39.03  53.09  39.33  36.66  50.32  55.82  46.20  7.5  6.6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向后填充缺失值\n",
    "data9 = data9.fillna(method='bfill')\n",
    "# 向前填充缺失值\n",
    "data9 = data9.fillna(method='ffill')\n",
    "data9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3ecb7b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>香港</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>34.79</td>\n",
       "      <td>44.1</td>\n",
       "      <td>51.25</td>\n",
       "      <td>60.89</td>\n",
       "      <td>53.06</td>\n",
       "      <td>31.00</td>\n",
       "      <td>55.70</td>\n",
       "      <td>48.95</td>\n",
       "      <td>55.22</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>40.532727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>34.50</td>\n",
       "      <td>44.1</td>\n",
       "      <td>50.84</td>\n",
       "      <td>60.69</td>\n",
       "      <td>52.07</td>\n",
       "      <td>33.60</td>\n",
       "      <td>54.84</td>\n",
       "      <td>48.33</td>\n",
       "      <td>53.10</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>40.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>32.88</td>\n",
       "      <td>41.9</td>\n",
       "      <td>47.90</td>\n",
       "      <td>60.61</td>\n",
       "      <td>48.60</td>\n",
       "      <td>35.70</td>\n",
       "      <td>54.76</td>\n",
       "      <td>45.86</td>\n",
       "      <td>52.90</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.264545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>32.03</td>\n",
       "      <td>40.7</td>\n",
       "      <td>46.65</td>\n",
       "      <td>59.60</td>\n",
       "      <td>48.20</td>\n",
       "      <td>40.20</td>\n",
       "      <td>54.69</td>\n",
       "      <td>44.37</td>\n",
       "      <td>53.21</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>39.113636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>31.74</td>\n",
       "      <td>40.2</td>\n",
       "      <td>45.16</td>\n",
       "      <td>60.76</td>\n",
       "      <td>46.35</td>\n",
       "      <td>41.00</td>\n",
       "      <td>53.40</td>\n",
       "      <td>46.06</td>\n",
       "      <td>53.00</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>39.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>30.17</td>\n",
       "      <td>39.0</td>\n",
       "      <td>44.14</td>\n",
       "      <td>59.27</td>\n",
       "      <td>45.67</td>\n",
       "      <td>41.40</td>\n",
       "      <td>51.73</td>\n",
       "      <td>45.26</td>\n",
       "      <td>52.06</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>38.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>28.40</td>\n",
       "      <td>37.6</td>\n",
       "      <td>43.16</td>\n",
       "      <td>58.14</td>\n",
       "      <td>44.86</td>\n",
       "      <td>39.50</td>\n",
       "      <td>50.81</td>\n",
       "      <td>45.25</td>\n",
       "      <td>50.21</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>37.457273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>26.88</td>\n",
       "      <td>37.4</td>\n",
       "      <td>42.36</td>\n",
       "      <td>55.63</td>\n",
       "      <td>46.59</td>\n",
       "      <td>37.50</td>\n",
       "      <td>49.55</td>\n",
       "      <td>47.06</td>\n",
       "      <td>48.05</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>36.829091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>26.13</td>\n",
       "      <td>36.4</td>\n",
       "      <td>43.14</td>\n",
       "      <td>54.03</td>\n",
       "      <td>45.85</td>\n",
       "      <td>35.90</td>\n",
       "      <td>49.55</td>\n",
       "      <td>47.24</td>\n",
       "      <td>46.67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>36.273636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>23.96</td>\n",
       "      <td>35.3</td>\n",
       "      <td>38.20</td>\n",
       "      <td>53.97</td>\n",
       "      <td>38.20</td>\n",
       "      <td>34.35</td>\n",
       "      <td>47.70</td>\n",
       "      <td>54.70</td>\n",
       "      <td>46.40</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>35.170909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>23.30</td>\n",
       "      <td>34.3</td>\n",
       "      <td>36.67</td>\n",
       "      <td>51.67</td>\n",
       "      <td>36.49</td>\n",
       "      <td>33.38</td>\n",
       "      <td>45.80</td>\n",
       "      <td>51.55</td>\n",
       "      <td>46.23</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>33.953636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>24.01</td>\n",
       "      <td>34.5</td>\n",
       "      <td>36.76</td>\n",
       "      <td>52.59</td>\n",
       "      <td>39.89</td>\n",
       "      <td>36.15</td>\n",
       "      <td>48.00</td>\n",
       "      <td>55.99</td>\n",
       "      <td>46.24</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>35.293636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>24.09</td>\n",
       "      <td>34.6</td>\n",
       "      <td>39.03</td>\n",
       "      <td>53.09</td>\n",
       "      <td>39.33</td>\n",
       "      <td>36.66</td>\n",
       "      <td>50.32</td>\n",
       "      <td>55.82</td>\n",
       "      <td>46.20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>35.749091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         广州    深圳     珠海     佛山     江门     肇庆     惠州     东莞     中山   香港   澳门  \\\n",
       "年                                                                              \n",
       "2010  34.79  44.1  51.25  60.89  53.06  31.00  55.70  48.95  55.22  6.7  4.2   \n",
       "2011  34.50  44.1  50.84  60.69  52.07  33.60  54.84  48.33  53.10  6.7  4.2   \n",
       "2012  32.88  41.9  47.90  60.61  48.60  35.70  54.76  45.86  52.90  6.8  4.0   \n",
       "2013  32.03  40.7  46.65  59.60  48.20  40.20  54.69  44.37  53.21  6.9  3.7   \n",
       "2014  31.74  40.2  45.16  60.76  46.35  41.00  53.40  46.06  53.00  7.1  5.0   \n",
       "2015  30.17  39.0  44.14  59.27  45.67  41.40  51.73  45.26  52.06  7.1  7.7   \n",
       "2016  28.40  37.6  43.16  58.14  44.86  39.50  50.81  45.25  50.21  7.5  6.6   \n",
       "2017  26.88  37.4  42.36  55.63  46.59  37.50  49.55  47.06  48.05  7.5  6.6   \n",
       "2018  26.13  36.4  43.14  54.03  45.85  35.90  49.55  47.24  46.67  7.5  6.6   \n",
       "2019  23.96  35.3  38.20  53.97  38.20  34.35  47.70  54.70  46.40  7.5  6.6   \n",
       "2020  23.30  34.3  36.67  51.67  36.49  33.38  45.80  51.55  46.23  7.5  6.6   \n",
       "2021  24.01  34.5  36.76  52.59  39.89  36.15  48.00  55.99  46.24  7.5  6.6   \n",
       "2022  24.09  34.6  39.03  53.09  39.33  36.66  50.32  55.82  46.20  7.5  6.6   \n",
       "\n",
       "            粤港澳  \n",
       "年                \n",
       "2010  40.532727  \n",
       "2011  40.270000  \n",
       "2012  39.264545  \n",
       "2013  39.113636  \n",
       "2014  39.070000  \n",
       "2015  38.500000  \n",
       "2016  37.457273  \n",
       "2017  36.829091  \n",
       "2018  36.273636  \n",
       "2019  35.170909  \n",
       "2020  33.953636  \n",
       "2021  35.293636  \n",
       "2022  35.749091  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data9['粤港澳']=data9.sum(axis=1)/11\n",
    "data9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26c1590",
   "metadata": {},
   "source": [
    "### x9 专利申请数（科技）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3f42e3b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>香港</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>13990.0</td>\n",
       "      <td>36249.0</td>\n",
       "      <td>2244.0</td>\n",
       "      <td>15340.0</td>\n",
       "      <td>4187.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>14406.0</td>\n",
       "      <td>6901.0</td>\n",
       "      <td>1511</td>\n",
       "      <td>14</td>\n",
       "      <td>96421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>16530.0</td>\n",
       "      <td>42279.0</td>\n",
       "      <td>2778.0</td>\n",
       "      <td>15340.0</td>\n",
       "      <td>4916.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>1761.0</td>\n",
       "      <td>19106.0</td>\n",
       "      <td>8699.0</td>\n",
       "      <td>1511</td>\n",
       "      <td>14</td>\n",
       "      <td>113485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>20803.0</td>\n",
       "      <td>49430.0</td>\n",
       "      <td>3554.0</td>\n",
       "      <td>17846.0</td>\n",
       "      <td>5844.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2889.0</td>\n",
       "      <td>21654.0</td>\n",
       "      <td>12031.0</td>\n",
       "      <td>1511</td>\n",
       "      <td>14</td>\n",
       "      <td>136382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>28097.0</td>\n",
       "      <td>63522.0</td>\n",
       "      <td>5594.0</td>\n",
       "      <td>20391.0</td>\n",
       "      <td>7697.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>6029.0</td>\n",
       "      <td>24455.0</td>\n",
       "      <td>14135.0</td>\n",
       "      <td>1511</td>\n",
       "      <td>14</td>\n",
       "      <td>171848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>33387.0</td>\n",
       "      <td>73130.0</td>\n",
       "      <td>7097.0</td>\n",
       "      <td>22063.0</td>\n",
       "      <td>8163.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>9894.0</td>\n",
       "      <td>29199.0</td>\n",
       "      <td>18400.0</td>\n",
       "      <td>1622</td>\n",
       "      <td>20</td>\n",
       "      <td>203514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>39751.0</td>\n",
       "      <td>80657.0</td>\n",
       "      <td>8017.0</td>\n",
       "      <td>27194.0</td>\n",
       "      <td>8439.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>15168.0</td>\n",
       "      <td>29012.0</td>\n",
       "      <td>21815.0</td>\n",
       "      <td>1744</td>\n",
       "      <td>34</td>\n",
       "      <td>232437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>46330.0</td>\n",
       "      <td>82254.0</td>\n",
       "      <td>8998.0</td>\n",
       "      <td>29701.0</td>\n",
       "      <td>8345.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>18359.0</td>\n",
       "      <td>28431.0</td>\n",
       "      <td>24618.0</td>\n",
       "      <td>1833</td>\n",
       "      <td>56</td>\n",
       "      <td>249685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>63366.0</td>\n",
       "      <td>105481.0</td>\n",
       "      <td>11334.0</td>\n",
       "      <td>39790.0</td>\n",
       "      <td>9555.0</td>\n",
       "      <td>773.0</td>\n",
       "      <td>21408.0</td>\n",
       "      <td>38094.0</td>\n",
       "      <td>27864.0</td>\n",
       "      <td>1943</td>\n",
       "      <td>97</td>\n",
       "      <td>319705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>99070.0</td>\n",
       "      <td>145294.0</td>\n",
       "      <td>17651.0</td>\n",
       "      <td>56456.0</td>\n",
       "      <td>13365.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>26123.0</td>\n",
       "      <td>56653.0</td>\n",
       "      <td>35248.0</td>\n",
       "      <td>2135</td>\n",
       "      <td>110</td>\n",
       "      <td>453143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>118332.0</td>\n",
       "      <td>177103.0</td>\n",
       "      <td>20737.0</td>\n",
       "      <td>73948.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>30448.0</td>\n",
       "      <td>81275.0</td>\n",
       "      <td>42168.0</td>\n",
       "      <td>2343</td>\n",
       "      <td>194</td>\n",
       "      <td>565786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>173124.0</td>\n",
       "      <td>228608.0</td>\n",
       "      <td>31167.0</td>\n",
       "      <td>89388.0</td>\n",
       "      <td>19748.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>21643.0</td>\n",
       "      <td>97030.0</td>\n",
       "      <td>49041.0</td>\n",
       "      <td>2205</td>\n",
       "      <td>136</td>\n",
       "      <td>714074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>177223.0</td>\n",
       "      <td>261502.0</td>\n",
       "      <td>33137.0</td>\n",
       "      <td>81011.0</td>\n",
       "      <td>20475.0</td>\n",
       "      <td>7031.0</td>\n",
       "      <td>22701.0</td>\n",
       "      <td>83217.0</td>\n",
       "      <td>43066.0</td>\n",
       "      <td>2182</td>\n",
       "      <td>160</td>\n",
       "      <td>731705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>216043.0</td>\n",
       "      <td>310206.0</td>\n",
       "      <td>33020.0</td>\n",
       "      <td>81782.0</td>\n",
       "      <td>20475.0</td>\n",
       "      <td>8131.0</td>\n",
       "      <td>22701.0</td>\n",
       "      <td>95959.0</td>\n",
       "      <td>43066.0</td>\n",
       "      <td>2131</td>\n",
       "      <td>35</td>\n",
       "      <td>833549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>287891.0</td>\n",
       "      <td>357372.0</td>\n",
       "      <td>37754.0</td>\n",
       "      <td>93892.0</td>\n",
       "      <td>20475.0</td>\n",
       "      <td>8131.0</td>\n",
       "      <td>22701.0</td>\n",
       "      <td>95959.0</td>\n",
       "      <td>43066.0</td>\n",
       "      <td>3261</td>\n",
       "      <td>163</td>\n",
       "      <td>970665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>287891.0</td>\n",
       "      <td>357372.0</td>\n",
       "      <td>37754.0</td>\n",
       "      <td>93892.0</td>\n",
       "      <td>20475.0</td>\n",
       "      <td>8131.0</td>\n",
       "      <td>22701.0</td>\n",
       "      <td>95959.0</td>\n",
       "      <td>43066.0</td>\n",
       "      <td>2459</td>\n",
       "      <td>27</td>\n",
       "      <td>969727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            广州        深圳       珠海       佛山       江门      肇庆       惠州       东莞  \\\n",
       "年                                                                               \n",
       "2008   13990.0   36249.0   2244.0  15340.0   4187.0   419.0   1160.0  14406.0   \n",
       "2009   16530.0   42279.0   2778.0  15340.0   4916.0   551.0   1761.0  19106.0   \n",
       "2010   20803.0   49430.0   3554.0  17846.0   5844.0   806.0   2889.0  21654.0   \n",
       "2011   28097.0   63522.0   5594.0  20391.0   7697.0   403.0   6029.0  24455.0   \n",
       "2012   33387.0   73130.0   7097.0  22063.0   8163.0   539.0   9894.0  29199.0   \n",
       "2013   39751.0   80657.0   8017.0  27194.0   8439.0   606.0  15168.0  29012.0   \n",
       "2014   46330.0   82254.0   8998.0  29701.0   8345.0   760.0  18359.0  28431.0   \n",
       "2015   63366.0  105481.0  11334.0  39790.0   9555.0   773.0  21408.0  38094.0   \n",
       "2016   99070.0  145294.0  17651.0  56456.0  13365.0  1038.0  26123.0  56653.0   \n",
       "2017  118332.0  177103.0  20737.0  73948.0  17966.0  1272.0  30448.0  81275.0   \n",
       "2018  173124.0  228608.0  31167.0  89388.0  19748.0  1984.0  21643.0  97030.0   \n",
       "2019  177223.0  261502.0  33137.0  81011.0  20475.0  7031.0  22701.0  83217.0   \n",
       "2020  216043.0  310206.0  33020.0  81782.0  20475.0  8131.0  22701.0  95959.0   \n",
       "2021  287891.0  357372.0  37754.0  93892.0  20475.0  8131.0  22701.0  95959.0   \n",
       "2022  287891.0  357372.0  37754.0  93892.0  20475.0  8131.0  22701.0  95959.0   \n",
       "\n",
       "           中山    香港   澳门       粤港澳  \n",
       "年                                   \n",
       "2008   6901.0  1511   14   96421.0  \n",
       "2009   8699.0  1511   14  113485.0  \n",
       "2010  12031.0  1511   14  136382.0  \n",
       "2011  14135.0  1511   14  171848.0  \n",
       "2012  18400.0  1622   20  203514.0  \n",
       "2013  21815.0  1744   34  232437.0  \n",
       "2014  24618.0  1833   56  249685.0  \n",
       "2015  27864.0  1943   97  319705.0  \n",
       "2016  35248.0  2135  110  453143.0  \n",
       "2017  42168.0  2343  194  565786.0  \n",
       "2018  49041.0  2205  136  714074.0  \n",
       "2019  43066.0  2182  160  731705.0  \n",
       "2020  43066.0  2131   35  833549.0  \n",
       "2021  43066.0  3261  163  970665.0  \n",
       "2022  43066.0  2459   27  969727.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data10 = pd.read_excel(r\"粤港澳大湾区专利申请数.xlsx\",skiprows=3,index_col=0)\n",
    "data10 = data10.drop([data10.index[0], data10.index[1]])\n",
    "# 填充缺失值\n",
    "data10 = data10.fillna(method='bfill')\n",
    "data10 = data10.fillna(method='ffill')\n",
    "data10['粤港澳']=data10.sum(axis=1)\n",
    "data10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd19332b",
   "metadata": {},
   "source": [
    "### x10 港口货物吞吐量（交通）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7587afc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>深圳</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>中山</th>\n",
       "      <th>东莞</th>\n",
       "      <th>香港</th>\n",
       "      <th>澳门</th>\n",
       "      <th>惠州</th>\n",
       "      <th>粤港澳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>19828.0</td>\n",
       "      <td>23659.0</td>\n",
       "      <td>12720.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>5138.0</td>\n",
       "      <td>6557.0</td>\n",
       "      <td>3824.0</td>\n",
       "      <td>14163.0</td>\n",
       "      <td>249286.0</td>\n",
       "      <td>3894.0</td>\n",
       "      <td>5294.0</td>\n",
       "      <td>353812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>18063.0</td>\n",
       "      <td>14188.0</td>\n",
       "      <td>10678.0</td>\n",
       "      <td>9145.0</td>\n",
       "      <td>3718.0</td>\n",
       "      <td>2389.0</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>213730.0</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>5357.0</td>\n",
       "      <td>285543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>17845.0</td>\n",
       "      <td>14284.0</td>\n",
       "      <td>8819.0</td>\n",
       "      <td>10287.0</td>\n",
       "      <td>5273.0</td>\n",
       "      <td>1607.0</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>2217.0</td>\n",
       "      <td>192104.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>2407.0</td>\n",
       "      <td>258077.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>16924.0</td>\n",
       "      <td>14517.0</td>\n",
       "      <td>9192.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>5359.0</td>\n",
       "      <td>1782.0</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>174867.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>235381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20--01</th>\n",
       "      <td>4310.0</td>\n",
       "      <td>5037.0</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>817.0</td>\n",
       "      <td>3259.0</td>\n",
       "      <td>57165.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>79359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20--02</th>\n",
       "      <td>4893.0</td>\n",
       "      <td>7712.0</td>\n",
       "      <td>2778.0</td>\n",
       "      <td>2244.0</td>\n",
       "      <td>1502.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>4954.0</td>\n",
       "      <td>67456.0</td>\n",
       "      <td>1143.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>96986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20--03</th>\n",
       "      <td>5105.0</td>\n",
       "      <td>5767.0</td>\n",
       "      <td>3334.0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1137.0</td>\n",
       "      <td>3515.0</td>\n",
       "      <td>63328.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>90317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20--04</th>\n",
       "      <td>5520.0</td>\n",
       "      <td>5142.0</td>\n",
       "      <td>3952.0</td>\n",
       "      <td>2362.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>2435.0</td>\n",
       "      <td>61337.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>87148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21--01</th>\n",
       "      <td>3744.0</td>\n",
       "      <td>3894.0</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>849.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>49174.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>1312.0</td>\n",
       "      <td>65823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21--02</th>\n",
       "      <td>4310.0</td>\n",
       "      <td>3719.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>55012.0</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>73506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21--03</th>\n",
       "      <td>5212.0</td>\n",
       "      <td>3209.0</td>\n",
       "      <td>3071.0</td>\n",
       "      <td>2779.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>55506.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>74158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21--04</th>\n",
       "      <td>4797.0</td>\n",
       "      <td>3366.0</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>54038.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>72056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22--01</th>\n",
       "      <td>3190.0</td>\n",
       "      <td>3494.0</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>42406.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>55278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22--02</th>\n",
       "      <td>5517.0</td>\n",
       "      <td>3813.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>3072.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>52274.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>70869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22--03</th>\n",
       "      <td>4457.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>2213.0</td>\n",
       "      <td>2562.0</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>49350.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>66257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22--04</th>\n",
       "      <td>4681.0</td>\n",
       "      <td>3752.0</td>\n",
       "      <td>2595.0</td>\n",
       "      <td>2854.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>48074.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>65671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23--01</th>\n",
       "      <td>4232.0</td>\n",
       "      <td>3376.0</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>41219.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>55561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23--02</th>\n",
       "      <td>3829.0</td>\n",
       "      <td>3588.0</td>\n",
       "      <td>2258.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>44504.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>59064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23--03</th>\n",
       "      <td>4411.0</td>\n",
       "      <td>3589.0</td>\n",
       "      <td>2538.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>45305.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>61282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23--04</th>\n",
       "      <td>4452.0</td>\n",
       "      <td>3965.0</td>\n",
       "      <td>2448.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>43839.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>59473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24--01</th>\n",
       "      <td>4026.0</td>\n",
       "      <td>4031.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>2391.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>42798.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>58961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24--02</th>\n",
       "      <td>4077.0</td>\n",
       "      <td>4407.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>3193.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>45815.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>63686.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             广州       珠海       佛山       深圳      江门      肇庆      中山       东莞  \\\n",
       "时间                                                                            \n",
       "2020    19828.0  23659.0  12720.0   9449.0  5138.0  6557.0  3824.0  14163.0   \n",
       "2021    18063.0  14188.0  10678.0   9145.0  3718.0  2389.0  2429.0   2496.0   \n",
       "2022    17845.0  14284.0   8819.0  10287.0  5273.0  1607.0  2119.0   2217.0   \n",
       "2023    16924.0  14517.0   9192.0   8115.0  5359.0  1782.0  1749.0   1476.0   \n",
       "20--01   4310.0   5037.0   2655.0   2160.0  1112.0   949.0   817.0   3259.0   \n",
       "20--02   4893.0   7712.0   2778.0   2244.0  1502.0  1899.0   876.0   4954.0   \n",
       "20--03   5105.0   5767.0   3334.0   2683.0  1278.0  1975.0  1137.0   3515.0   \n",
       "20--04   5520.0   5142.0   3952.0   2362.0  1247.0  1734.0   994.0   2435.0   \n",
       "21--01   3744.0   3894.0   2354.0   1629.0   617.0   849.0   625.0    696.0   \n",
       "21--02   4310.0   3719.0   2720.0   2202.0  1114.0   527.0   607.0    696.0   \n",
       "21--03   5212.0   3209.0   3071.0   2779.0   835.0   459.0   630.0    477.0   \n",
       "21--04   4797.0   3366.0   2533.0   2535.0  1152.0   554.0   567.0    627.0   \n",
       "22--01   3190.0   3494.0   1605.0   1799.0   415.0   257.0   457.0    345.0   \n",
       "22--02   5517.0   3813.0   2405.0   3072.0  1124.0   475.0   639.0    565.0   \n",
       "22--03   4457.0   3225.0   2213.0   2562.0  2035.0   503.0   498.0    648.0   \n",
       "22--04   4681.0   3752.0   2595.0   2854.0  1698.0   372.0   525.0    660.0   \n",
       "23--01   4232.0   3376.0   1948.0   1983.0  1171.0   321.0   408.0    408.0   \n",
       "23--02   3829.0   3588.0   2258.0   2054.0  1247.0   447.0   465.0    365.0   \n",
       "23--03   4411.0   3589.0   2538.0   2153.0  1728.0   515.0   391.0    368.0   \n",
       "23--04   4452.0   3965.0   2448.0   1924.0  1213.0   498.0   485.0    335.0   \n",
       "24--01   4026.0   4031.0   2514.0   1730.0  2391.0   324.0   425.0    409.0   \n",
       "24--02   4077.0   4407.0   2276.0   2255.0  3193.0   397.0   415.0    424.0   \n",
       "\n",
       "              香港      澳门      惠州       粤港澳  \n",
       "时间                                          \n",
       "2020    249286.0  3894.0  5294.0  353812.0  \n",
       "2021    213730.0  3350.0  5357.0  285543.0  \n",
       "2022    192104.0  1115.0  2407.0  258077.0  \n",
       "2023    174867.0   939.0   461.0  235381.0  \n",
       "20--01   57165.0   892.0  1003.0   79359.0  \n",
       "20--02   67456.0  1143.0  1529.0   96986.0  \n",
       "20--03   63328.0   881.0  1314.0   90317.0  \n",
       "20--04   61337.0   977.0  1448.0   87148.0  \n",
       "21--01   49174.0   929.0  1312.0   65823.0  \n",
       "21--02   55012.0  1238.0  1361.0   73506.0  \n",
       "21--03   55506.0   736.0  1244.0   74158.0  \n",
       "21--04   54038.0   447.0  1440.0   72056.0  \n",
       "22--01   42406.0   333.0   977.0   55278.0  \n",
       "22--02   52274.0   329.0   656.0   70869.0  \n",
       "22--03   49350.0   210.0   556.0   66257.0  \n",
       "22--04   48074.0   243.0   217.0   65671.0  \n",
       "23--01   41219.0   274.0   221.0   55561.0  \n",
       "23--02   44504.0   228.0    79.0   59064.0  \n",
       "23--03   45305.0   225.0    59.0   61282.0  \n",
       "23--04   43839.0   212.0   102.0   59473.0  \n",
       "24--01   42798.0   150.0   163.0   58961.0  \n",
       "24--02   45815.0   172.0   255.0   63686.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data11 = pd.read_excel(r\"粤港澳港口货物吞吐量.xlsx\",skiprows=4)\n",
    "#df_port_throughput = df_port_throughput.drop([df_port_throughput.index[0], df_port_throughput.index[1]])\n",
    "# 删除前四列\n",
    "data11 = data11.drop(data11.columns[:2], axis=1)[:22]\n",
    "data11.set_index(data11.columns[0], inplace=True)\n",
    "data11.index.name = '时间'\n",
    "data11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862c9fb9",
   "metadata": {},
   "source": [
    "### x11 高等学校在校生数（教育）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "32d37c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>广州</th>\n",
       "      <th>深圳</th>\n",
       "      <th>珠海</th>\n",
       "      <th>江门</th>\n",
       "      <th>佛山</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>中山</th>\n",
       "      <th>东莞</th>\n",
       "      <th>香港</th>\n",
       "      <th>澳门</th>\n",
       "      <th>粤港澳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>843934</td>\n",
       "      <td>70004</td>\n",
       "      <td>116952</td>\n",
       "      <td>26624</td>\n",
       "      <td>44551</td>\n",
       "      <td>58675</td>\n",
       "      <td>22007</td>\n",
       "      <td>36338</td>\n",
       "      <td>45081</td>\n",
       "      <td>329700</td>\n",
       "      <td>22107</td>\n",
       "      <td>1615973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>896123</td>\n",
       "      <td>75570</td>\n",
       "      <td>123206</td>\n",
       "      <td>30669</td>\n",
       "      <td>45778</td>\n",
       "      <td>63657</td>\n",
       "      <td>24300</td>\n",
       "      <td>36326</td>\n",
       "      <td>52381</td>\n",
       "      <td>329700</td>\n",
       "      <td>22107</td>\n",
       "      <td>1699817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>939208</td>\n",
       "      <td>82401</td>\n",
       "      <td>127115</td>\n",
       "      <td>34345</td>\n",
       "      <td>47297</td>\n",
       "      <td>66648</td>\n",
       "      <td>27012</td>\n",
       "      <td>38641</td>\n",
       "      <td>60877</td>\n",
       "      <td>330300</td>\n",
       "      <td>22107</td>\n",
       "      <td>1775951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>983051</td>\n",
       "      <td>87674</td>\n",
       "      <td>132000</td>\n",
       "      <td>38722</td>\n",
       "      <td>46706</td>\n",
       "      <td>75810</td>\n",
       "      <td>30183</td>\n",
       "      <td>39964</td>\n",
       "      <td>69866</td>\n",
       "      <td>324600</td>\n",
       "      <td>25400</td>\n",
       "      <td>1853976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1019291</td>\n",
       "      <td>90511</td>\n",
       "      <td>132000</td>\n",
       "      <td>39490</td>\n",
       "      <td>49395</td>\n",
       "      <td>81441</td>\n",
       "      <td>34174</td>\n",
       "      <td>39951</td>\n",
       "      <td>114626</td>\n",
       "      <td>327900</td>\n",
       "      <td>28900</td>\n",
       "      <td>1957679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1043221</td>\n",
       "      <td>91883</td>\n",
       "      <td>133626</td>\n",
       "      <td>40055</td>\n",
       "      <td>49994</td>\n",
       "      <td>86556</td>\n",
       "      <td>37229</td>\n",
       "      <td>48661</td>\n",
       "      <td>112603</td>\n",
       "      <td>325600</td>\n",
       "      <td>30320</td>\n",
       "      <td>1999748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1057281</td>\n",
       "      <td>96702</td>\n",
       "      <td>136829</td>\n",
       "      <td>41059</td>\n",
       "      <td>122005</td>\n",
       "      <td>88520</td>\n",
       "      <td>39212</td>\n",
       "      <td>53611</td>\n",
       "      <td>118416</td>\n",
       "      <td>321800</td>\n",
       "      <td>32310</td>\n",
       "      <td>2107745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1067335</td>\n",
       "      <td>103829</td>\n",
       "      <td>138957</td>\n",
       "      <td>44183</td>\n",
       "      <td>124245</td>\n",
       "      <td>97195</td>\n",
       "      <td>44018</td>\n",
       "      <td>56996</td>\n",
       "      <td>121408</td>\n",
       "      <td>313800</td>\n",
       "      <td>34279</td>\n",
       "      <td>2146245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1086407</td>\n",
       "      <td>113214</td>\n",
       "      <td>139291</td>\n",
       "      <td>47976</td>\n",
       "      <td>129945</td>\n",
       "      <td>114111</td>\n",
       "      <td>49160</td>\n",
       "      <td>53118</td>\n",
       "      <td>124439</td>\n",
       "      <td>312500</td>\n",
       "      <td>36107</td>\n",
       "      <td>2206268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1140949</td>\n",
       "      <td>136184</td>\n",
       "      <td>143778</td>\n",
       "      <td>55980</td>\n",
       "      <td>148311</td>\n",
       "      <td>145479</td>\n",
       "      <td>64976</td>\n",
       "      <td>56183</td>\n",
       "      <td>135279</td>\n",
       "      <td>305200</td>\n",
       "      <td>39093</td>\n",
       "      <td>2371412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1307144</td>\n",
       "      <td>145181</td>\n",
       "      <td>137812</td>\n",
       "      <td>67578</td>\n",
       "      <td>153207</td>\n",
       "      <td>138466</td>\n",
       "      <td>69906</td>\n",
       "      <td>54350</td>\n",
       "      <td>139334</td>\n",
       "      <td>303300</td>\n",
       "      <td>44052</td>\n",
       "      <td>2560330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>1412569</td>\n",
       "      <td>155264</td>\n",
       "      <td>138589</td>\n",
       "      <td>75182</td>\n",
       "      <td>167602</td>\n",
       "      <td>192334</td>\n",
       "      <td>73648</td>\n",
       "      <td>58010</td>\n",
       "      <td>152936</td>\n",
       "      <td>303900</td>\n",
       "      <td>49594</td>\n",
       "      <td>2779628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>1489276</td>\n",
       "      <td>160800</td>\n",
       "      <td>138589</td>\n",
       "      <td>75182</td>\n",
       "      <td>157100</td>\n",
       "      <td>192334</td>\n",
       "      <td>67087</td>\n",
       "      <td>58010</td>\n",
       "      <td>158700</td>\n",
       "      <td>314400</td>\n",
       "      <td>55500</td>\n",
       "      <td>2866978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           广州      深圳      珠海     江门      佛山      肇庆     惠州     中山      东莞  \\\n",
       "年                                                                            \n",
       "2011   843934   70004  116952  26624   44551   58675  22007  36338   45081   \n",
       "2012   896123   75570  123206  30669   45778   63657  24300  36326   52381   \n",
       "2013   939208   82401  127115  34345   47297   66648  27012  38641   60877   \n",
       "2014   983051   87674  132000  38722   46706   75810  30183  39964   69866   \n",
       "2015  1019291   90511  132000  39490   49395   81441  34174  39951  114626   \n",
       "2016  1043221   91883  133626  40055   49994   86556  37229  48661  112603   \n",
       "2017  1057281   96702  136829  41059  122005   88520  39212  53611  118416   \n",
       "2018  1067335  103829  138957  44183  124245   97195  44018  56996  121408   \n",
       "2019  1086407  113214  139291  47976  129945  114111  49160  53118  124439   \n",
       "2020  1140949  136184  143778  55980  148311  145479  64976  56183  135279   \n",
       "2021  1307144  145181  137812  67578  153207  138466  69906  54350  139334   \n",
       "2022  1412569  155264  138589  75182  167602  192334  73648  58010  152936   \n",
       "2023  1489276  160800  138589  75182  157100  192334  67087  58010  158700   \n",
       "\n",
       "          香港     澳门      粤港澳  \n",
       "年                             \n",
       "2011  329700  22107  1615973  \n",
       "2012  329700  22107  1699817  \n",
       "2013  330300  22107  1775951  \n",
       "2014  324600  25400  1853976  \n",
       "2015  327900  28900  1957679  \n",
       "2016  325600  30320  1999748  \n",
       "2017  321800  32310  2107745  \n",
       "2018  313800  34279  2146245  \n",
       "2019  312500  36107  2206268  \n",
       "2020  305200  39093  2371412  \n",
       "2021  303300  44052  2560330  \n",
       "2022  303900  49594  2779628  \n",
       "2023  314400  55500  2866978  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data12 = pd.read_excel(r\"粤港澳高等学校在校生数.xlsx\",skiprows=3,index_col=0)\n",
    "#填充缺失值\n",
    "data12 = data12.fillna(method='bfill')\n",
    "data12 = data12.fillna(method='ffill')\n",
    "data12['粤港澳']=data12.sum(axis=1)\n",
    "data12 = data12.iloc[2:]\n",
    "data12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebc1624",
   "metadata": {},
   "source": [
    "### x12 旅游收入（旅游）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "626162f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>深圳</th>\n",
       "      <th>广州</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>737.32</td>\n",
       "      <td>1632.28</td>\n",
       "      <td>222.83</td>\n",
       "      <td>296.46</td>\n",
       "      <td>154.43</td>\n",
       "      <td>142.32</td>\n",
       "      <td>161.19</td>\n",
       "      <td>249.37</td>\n",
       "      <td>151.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>839.97</td>\n",
       "      <td>1972.35</td>\n",
       "      <td>235.83</td>\n",
       "      <td>365.72</td>\n",
       "      <td>185.29</td>\n",
       "      <td>179.49</td>\n",
       "      <td>184.15</td>\n",
       "      <td>306.35</td>\n",
       "      <td>180.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>955.98</td>\n",
       "      <td>2019.11</td>\n",
       "      <td>241.79</td>\n",
       "      <td>431.09</td>\n",
       "      <td>223.28</td>\n",
       "      <td>205.87</td>\n",
       "      <td>212.65</td>\n",
       "      <td>346.43</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1091.65</td>\n",
       "      <td>2781.27</td>\n",
       "      <td>270.65</td>\n",
       "      <td>496.27</td>\n",
       "      <td>279</td>\n",
       "      <td>221.17</td>\n",
       "      <td>273.2</td>\n",
       "      <td>374.6</td>\n",
       "      <td>210.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1244.96</td>\n",
       "      <td>3012.51</td>\n",
       "      <td>276.76</td>\n",
       "      <td>546.29</td>\n",
       "      <td>339.62</td>\n",
       "      <td>241.62</td>\n",
       "      <td>330.23</td>\n",
       "      <td>395.18</td>\n",
       "      <td>227.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1368.67</td>\n",
       "      <td>3207.92</td>\n",
       "      <td>317.08</td>\n",
       "      <td>624.72</td>\n",
       "      <td>409.9</td>\n",
       "      <td>285.75</td>\n",
       "      <td>364.14</td>\n",
       "      <td>445.4</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1485.45</td>\n",
       "      <td>3614.21</td>\n",
       "      <td>367.7</td>\n",
       "      <td>710.84</td>\n",
       "      <td>492.53</td>\n",
       "      <td>308.28</td>\n",
       "      <td>439.28</td>\n",
       "      <td>488.9</td>\n",
       "      <td>287.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1609.31</td>\n",
       "      <td>4008.19</td>\n",
       "      <td>466.15</td>\n",
       "      <td>809.13</td>\n",
       "      <td>586.83</td>\n",
       "      <td>322.99</td>\n",
       "      <td>500.36</td>\n",
       "      <td>529.37</td>\n",
       "      <td>294.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1715.17</td>\n",
       "      <td>4454.59</td>\n",
       "      <td>541.52</td>\n",
       "      <td>891.86</td>\n",
       "      <td>690.52</td>\n",
       "      <td>340.82</td>\n",
       "      <td>574.26</td>\n",
       "      <td>574.17</td>\n",
       "      <td>303.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1383.74</td>\n",
       "      <td>2679.07</td>\n",
       "      <td>187.59</td>\n",
       "      <td>325.05</td>\n",
       "      <td>103.54</td>\n",
       "      <td>68.72</td>\n",
       "      <td>205.78</td>\n",
       "      <td>358.63</td>\n",
       "      <td>120.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1598.98</td>\n",
       "      <td>2885.89</td>\n",
       "      <td>207.71</td>\n",
       "      <td>333.55</td>\n",
       "      <td>124.83</td>\n",
       "      <td>102.71</td>\n",
       "      <td>219.6</td>\n",
       "      <td>379.58</td>\n",
       "      <td>129.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>1161.5</td>\n",
       "      <td>2246.03</td>\n",
       "      <td>114.06</td>\n",
       "      <td>284.25</td>\n",
       "      <td>94.88</td>\n",
       "      <td>74.95</td>\n",
       "      <td>168.18</td>\n",
       "      <td>307.41</td>\n",
       "      <td>95.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           深圳       广州      珠海      佛山      江门      肇庆      惠州      东莞      中山\n",
       "年                                                                             \n",
       "2011   737.32  1632.28  222.83  296.46  154.43  142.32  161.19  249.37  151.41\n",
       "2012   839.97  1972.35  235.83  365.72  185.29  179.49  184.15  306.35  180.69\n",
       "2013   955.98  2019.11  241.79  431.09  223.28  205.87  212.65  346.43     198\n",
       "2014  1091.65  2781.27  270.65  496.27     279  221.17   273.2   374.6  210.23\n",
       "2015  1244.96  3012.51  276.76  546.29  339.62  241.62  330.23  395.18  227.44\n",
       "2016  1368.67  3207.92  317.08  624.72   409.9  285.75  364.14   445.4     247\n",
       "2017  1485.45  3614.21   367.7  710.84  492.53  308.28  439.28   488.9   287.4\n",
       "2018  1609.31  4008.19  466.15  809.13  586.83  322.99  500.36  529.37  294.11\n",
       "2019  1715.17  4454.59  541.52  891.86  690.52  340.82  574.26  574.17  303.78\n",
       "2020  1383.74  2679.07  187.59  325.05  103.54   68.72  205.78  358.63  120.45\n",
       "2021  1598.98  2885.89  207.71  333.55  124.83  102.71   219.6  379.58  129.58\n",
       "2022   1161.5  2246.03  114.06  284.25   94.88   74.95  168.18  307.41   95.16"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data13 = pd.read_excel(r\"九市_旅游总收入.xlsx\",skiprows=3,index_col=0)\n",
    "# 填充缺失值\n",
    "data13 = data13.fillna(method='bfill')\n",
    "data13 = data13.fillna(method='ffill')\n",
    "data13 = data13.iloc[2:]\n",
    "data13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5e469bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中国香港</th>\n",
       "      <th>中国澳门</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011年</th>\n",
       "      <td>3.316900e+10</td>\n",
       "      <td>3.111800e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012年</th>\n",
       "      <td>3.709800e+10</td>\n",
       "      <td>3.659300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013年</th>\n",
       "      <td>4.242600e+10</td>\n",
       "      <td>4.375500e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014年</th>\n",
       "      <td>4.635200e+10</td>\n",
       "      <td>4.341200e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015年</th>\n",
       "      <td>4.249100e+10</td>\n",
       "      <td>3.162000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016年</th>\n",
       "      <td>3.783800e+10</td>\n",
       "      <td>3.115500e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017年</th>\n",
       "      <td>3.817000e+10</td>\n",
       "      <td>3.659500e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018年</th>\n",
       "      <td>4.231300e+10</td>\n",
       "      <td>4.147800e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019年</th>\n",
       "      <td>3.269700e+10</td>\n",
       "      <td>4.116600e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020年</th>\n",
       "      <td>8.328000e+09</td>\n",
       "      <td>9.442000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021年</th>\n",
       "      <td>7.291000e+09</td>\n",
       "      <td>8.219000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022年</th>\n",
       "      <td>1.072000e+09</td>\n",
       "      <td>1.326000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               中国香港          中国澳门\n",
       "年                                \n",
       "2011年  3.316900e+10  3.111800e+10\n",
       "2012年  3.709800e+10  3.659300e+10\n",
       "2013年  4.242600e+10  4.375500e+10\n",
       "2014年  4.635200e+10  4.341200e+10\n",
       "2015年  4.249100e+10  3.162000e+10\n",
       "2016年  3.783800e+10  3.115500e+10\n",
       "2017年  3.817000e+10  3.659500e+10\n",
       "2018年  4.231300e+10  4.147800e+10\n",
       "2019年  3.269700e+10  4.116600e+10\n",
       "2020年  8.328000e+09  9.442000e+09\n",
       "2021年  7.291000e+09  8.219000e+09\n",
       "2022年  1.072000e+09  1.326000e+09"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rate = 7.1224\n",
    "data14 = pd.read_excel(r\"港澳_旅游收入.xls\",index_col=0)\n",
    "# 填充缺失值\n",
    "data14 = data14.fillna(method='bfill')\n",
    "data14 = data14.fillna(method='ffill')\n",
    "data14 = data14.iloc[:12]\n",
    "data14 = data14.dropna(axis=1, how='any')\n",
    "data14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bf04dbf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中国香港</th>\n",
       "      <th>中国澳门</th>\n",
       "      <th>中国香港（亿元）</th>\n",
       "      <th>中国澳门（亿元）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011年</th>\n",
       "      <td>3.316900e+10</td>\n",
       "      <td>3.111800e+10</td>\n",
       "      <td>2362.428856</td>\n",
       "      <td>2216.348432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012年</th>\n",
       "      <td>3.709800e+10</td>\n",
       "      <td>3.659300e+10</td>\n",
       "      <td>2642.267952</td>\n",
       "      <td>2606.299832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013年</th>\n",
       "      <td>4.242600e+10</td>\n",
       "      <td>4.375500e+10</td>\n",
       "      <td>3021.749424</td>\n",
       "      <td>3116.406120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014年</th>\n",
       "      <td>4.635200e+10</td>\n",
       "      <td>4.341200e+10</td>\n",
       "      <td>3301.374848</td>\n",
       "      <td>3091.976288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015年</th>\n",
       "      <td>4.249100e+10</td>\n",
       "      <td>3.162000e+10</td>\n",
       "      <td>3026.378984</td>\n",
       "      <td>2252.102880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016年</th>\n",
       "      <td>3.783800e+10</td>\n",
       "      <td>3.115500e+10</td>\n",
       "      <td>2694.973712</td>\n",
       "      <td>2218.983720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017年</th>\n",
       "      <td>3.817000e+10</td>\n",
       "      <td>3.659500e+10</td>\n",
       "      <td>2718.620080</td>\n",
       "      <td>2606.442280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018年</th>\n",
       "      <td>4.231300e+10</td>\n",
       "      <td>4.147800e+10</td>\n",
       "      <td>3013.701112</td>\n",
       "      <td>2954.229072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019年</th>\n",
       "      <td>3.269700e+10</td>\n",
       "      <td>4.116600e+10</td>\n",
       "      <td>2328.811128</td>\n",
       "      <td>2932.007184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020年</th>\n",
       "      <td>8.328000e+09</td>\n",
       "      <td>9.442000e+09</td>\n",
       "      <td>593.153472</td>\n",
       "      <td>672.497008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021年</th>\n",
       "      <td>7.291000e+09</td>\n",
       "      <td>8.219000e+09</td>\n",
       "      <td>519.294184</td>\n",
       "      <td>585.390056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022年</th>\n",
       "      <td>1.072000e+09</td>\n",
       "      <td>1.326000e+09</td>\n",
       "      <td>76.352128</td>\n",
       "      <td>94.443024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               中国香港          中国澳门     中国香港（亿元）     中国澳门（亿元）\n",
       "年                                                          \n",
       "2011年  3.316900e+10  3.111800e+10  2362.428856  2216.348432\n",
       "2012年  3.709800e+10  3.659300e+10  2642.267952  2606.299832\n",
       "2013年  4.242600e+10  4.375500e+10  3021.749424  3116.406120\n",
       "2014年  4.635200e+10  4.341200e+10  3301.374848  3091.976288\n",
       "2015年  4.249100e+10  3.162000e+10  3026.378984  2252.102880\n",
       "2016年  3.783800e+10  3.115500e+10  2694.973712  2218.983720\n",
       "2017年  3.817000e+10  3.659500e+10  2718.620080  2606.442280\n",
       "2018年  4.231300e+10  4.147800e+10  3013.701112  2954.229072\n",
       "2019年  3.269700e+10  4.116600e+10  2328.811128  2932.007184\n",
       "2020年  8.328000e+09  9.442000e+09   593.153472   672.497008\n",
       "2021年  7.291000e+09  8.219000e+09   519.294184   585.390056\n",
       "2022年  1.072000e+09  1.326000e+09    76.352128    94.443024"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data14['中国香港（亿元）'] = data14['中国香港'] * exchange_rate/100000000\n",
    "data14['中国澳门（亿元）'] = data14['中国澳门'] * exchange_rate/100000000\n",
    "data14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "95400789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>深圳</th>\n",
       "      <th>广州</th>\n",
       "      <th>珠海</th>\n",
       "      <th>佛山</th>\n",
       "      <th>江门</th>\n",
       "      <th>肇庆</th>\n",
       "      <th>惠州</th>\n",
       "      <th>东莞</th>\n",
       "      <th>中山</th>\n",
       "      <th>澳门</th>\n",
       "      <th>香港</th>\n",
       "      <th>粤港澳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>737.32</td>\n",
       "      <td>1632.28</td>\n",
       "      <td>222.83</td>\n",
       "      <td>296.46</td>\n",
       "      <td>154.43</td>\n",
       "      <td>142.32</td>\n",
       "      <td>161.19</td>\n",
       "      <td>249.37</td>\n",
       "      <td>151.41</td>\n",
       "      <td>2216.348432</td>\n",
       "      <td>2362.428856</td>\n",
       "      <td>8326.387288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>839.97</td>\n",
       "      <td>1972.35</td>\n",
       "      <td>235.83</td>\n",
       "      <td>365.72</td>\n",
       "      <td>185.29</td>\n",
       "      <td>179.49</td>\n",
       "      <td>184.15</td>\n",
       "      <td>306.35</td>\n",
       "      <td>180.69</td>\n",
       "      <td>2606.299832</td>\n",
       "      <td>2642.267952</td>\n",
       "      <td>9698.407784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>955.98</td>\n",
       "      <td>2019.11</td>\n",
       "      <td>241.79</td>\n",
       "      <td>431.09</td>\n",
       "      <td>223.28</td>\n",
       "      <td>205.87</td>\n",
       "      <td>212.65</td>\n",
       "      <td>346.43</td>\n",
       "      <td>198</td>\n",
       "      <td>3116.406120</td>\n",
       "      <td>3021.749424</td>\n",
       "      <td>10972.355544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1091.65</td>\n",
       "      <td>2781.27</td>\n",
       "      <td>270.65</td>\n",
       "      <td>496.27</td>\n",
       "      <td>279</td>\n",
       "      <td>221.17</td>\n",
       "      <td>273.2</td>\n",
       "      <td>374.6</td>\n",
       "      <td>210.23</td>\n",
       "      <td>3091.976288</td>\n",
       "      <td>3301.374848</td>\n",
       "      <td>12391.391136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1244.96</td>\n",
       "      <td>3012.51</td>\n",
       "      <td>276.76</td>\n",
       "      <td>546.29</td>\n",
       "      <td>339.62</td>\n",
       "      <td>241.62</td>\n",
       "      <td>330.23</td>\n",
       "      <td>395.18</td>\n",
       "      <td>227.44</td>\n",
       "      <td>2252.102880</td>\n",
       "      <td>3026.378984</td>\n",
       "      <td>11893.091864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1368.67</td>\n",
       "      <td>3207.92</td>\n",
       "      <td>317.08</td>\n",
       "      <td>624.72</td>\n",
       "      <td>409.9</td>\n",
       "      <td>285.75</td>\n",
       "      <td>364.14</td>\n",
       "      <td>445.4</td>\n",
       "      <td>247</td>\n",
       "      <td>2218.983720</td>\n",
       "      <td>2694.973712</td>\n",
       "      <td>12184.537432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1485.45</td>\n",
       "      <td>3614.21</td>\n",
       "      <td>367.7</td>\n",
       "      <td>710.84</td>\n",
       "      <td>492.53</td>\n",
       "      <td>308.28</td>\n",
       "      <td>439.28</td>\n",
       "      <td>488.9</td>\n",
       "      <td>287.4</td>\n",
       "      <td>2606.442280</td>\n",
       "      <td>2718.620080</td>\n",
       "      <td>13519.65236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1609.31</td>\n",
       "      <td>4008.19</td>\n",
       "      <td>466.15</td>\n",
       "      <td>809.13</td>\n",
       "      <td>586.83</td>\n",
       "      <td>322.99</td>\n",
       "      <td>500.36</td>\n",
       "      <td>529.37</td>\n",
       "      <td>294.11</td>\n",
       "      <td>2954.229072</td>\n",
       "      <td>3013.701112</td>\n",
       "      <td>15094.370184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1715.17</td>\n",
       "      <td>4454.59</td>\n",
       "      <td>541.52</td>\n",
       "      <td>891.86</td>\n",
       "      <td>690.52</td>\n",
       "      <td>340.82</td>\n",
       "      <td>574.26</td>\n",
       "      <td>574.17</td>\n",
       "      <td>303.78</td>\n",
       "      <td>2932.007184</td>\n",
       "      <td>2328.811128</td>\n",
       "      <td>15347.508312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1383.74</td>\n",
       "      <td>2679.07</td>\n",
       "      <td>187.59</td>\n",
       "      <td>325.05</td>\n",
       "      <td>103.54</td>\n",
       "      <td>68.72</td>\n",
       "      <td>205.78</td>\n",
       "      <td>358.63</td>\n",
       "      <td>120.45</td>\n",
       "      <td>672.497008</td>\n",
       "      <td>593.153472</td>\n",
       "      <td>6698.22048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1598.98</td>\n",
       "      <td>2885.89</td>\n",
       "      <td>207.71</td>\n",
       "      <td>333.55</td>\n",
       "      <td>124.83</td>\n",
       "      <td>102.71</td>\n",
       "      <td>219.6</td>\n",
       "      <td>379.58</td>\n",
       "      <td>129.58</td>\n",
       "      <td>585.390056</td>\n",
       "      <td>519.294184</td>\n",
       "      <td>7087.11424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>1161.5</td>\n",
       "      <td>2246.03</td>\n",
       "      <td>114.06</td>\n",
       "      <td>284.25</td>\n",
       "      <td>94.88</td>\n",
       "      <td>74.95</td>\n",
       "      <td>168.18</td>\n",
       "      <td>307.41</td>\n",
       "      <td>95.16</td>\n",
       "      <td>94.443024</td>\n",
       "      <td>76.352128</td>\n",
       "      <td>4717.215152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           深圳       广州      珠海      佛山      江门      肇庆      惠州      东莞  \\\n",
       "年                                                                        \n",
       "2011   737.32  1632.28  222.83  296.46  154.43  142.32  161.19  249.37   \n",
       "2012   839.97  1972.35  235.83  365.72  185.29  179.49  184.15  306.35   \n",
       "2013   955.98  2019.11  241.79  431.09  223.28  205.87  212.65  346.43   \n",
       "2014  1091.65  2781.27  270.65  496.27     279  221.17   273.2   374.6   \n",
       "2015  1244.96  3012.51  276.76  546.29  339.62  241.62  330.23  395.18   \n",
       "2016  1368.67  3207.92  317.08  624.72   409.9  285.75  364.14   445.4   \n",
       "2017  1485.45  3614.21   367.7  710.84  492.53  308.28  439.28   488.9   \n",
       "2018  1609.31  4008.19  466.15  809.13  586.83  322.99  500.36  529.37   \n",
       "2019  1715.17  4454.59  541.52  891.86  690.52  340.82  574.26  574.17   \n",
       "2020  1383.74  2679.07  187.59  325.05  103.54   68.72  205.78  358.63   \n",
       "2021  1598.98  2885.89  207.71  333.55  124.83  102.71   219.6  379.58   \n",
       "2022   1161.5  2246.03  114.06  284.25   94.88   74.95  168.18  307.41   \n",
       "\n",
       "          中山           澳门           香港           粤港澳  \n",
       "年                                                     \n",
       "2011  151.41  2216.348432  2362.428856   8326.387288  \n",
       "2012  180.69  2606.299832  2642.267952   9698.407784  \n",
       "2013     198  3116.406120  3021.749424  10972.355544  \n",
       "2014  210.23  3091.976288  3301.374848  12391.391136  \n",
       "2015  227.44  2252.102880  3026.378984  11893.091864  \n",
       "2016     247  2218.983720  2694.973712  12184.537432  \n",
       "2017   287.4  2606.442280  2718.620080   13519.65236  \n",
       "2018  294.11  2954.229072  3013.701112  15094.370184  \n",
       "2019  303.78  2932.007184  2328.811128  15347.508312  \n",
       "2020  120.45   672.497008   593.153472    6698.22048  \n",
       "2021  129.58   585.390056   519.294184    7087.11424  \n",
       "2022   95.16    94.443024    76.352128   4717.215152  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#合并数据并求和\n",
    "data13['澳门']=data14['中国澳门（亿元）'].values\n",
    "data13['香港']=data14['中国香港（亿元）'].values\n",
    "data13['粤港澳']=data13.sum(axis=1)\n",
    "data13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7ad51053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>本地生产总值(亿元)</th>\n",
       "      <th>博物馆数量(所)</th>\n",
       "      <th>工业增加值占GDP比重(%)</th>\n",
       "      <th>专利申请数(项)</th>\n",
       "      <th>高等学校在校生数(人)</th>\n",
       "      <th>旅游收入(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>64345.778</td>\n",
       "      <td>135</td>\n",
       "      <td>40.270000</td>\n",
       "      <td>171848.0</td>\n",
       "      <td>1615973</td>\n",
       "      <td>8326.387288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>69786.0328</td>\n",
       "      <td>141</td>\n",
       "      <td>39.264545</td>\n",
       "      <td>203514.0</td>\n",
       "      <td>1699817</td>\n",
       "      <td>9698.407784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>76850.877</td>\n",
       "      <td>168</td>\n",
       "      <td>39.113636</td>\n",
       "      <td>232437.0</td>\n",
       "      <td>1775951</td>\n",
       "      <td>10972.355544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>82583.13</td>\n",
       "      <td>178</td>\n",
       "      <td>39.070000</td>\n",
       "      <td>249685.0</td>\n",
       "      <td>1853976</td>\n",
       "      <td>12391.391136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>87842.928</td>\n",
       "      <td>186</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>319705.0</td>\n",
       "      <td>1957679</td>\n",
       "      <td>11893.091864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>94353.4676</td>\n",
       "      <td>186</td>\n",
       "      <td>37.457273</td>\n",
       "      <td>453143.0</td>\n",
       "      <td>1999748</td>\n",
       "      <td>12184.537432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>103061.1102</td>\n",
       "      <td>187</td>\n",
       "      <td>36.829091</td>\n",
       "      <td>565786.0</td>\n",
       "      <td>2107745</td>\n",
       "      <td>13519.65236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>110536.4098</td>\n",
       "      <td>188</td>\n",
       "      <td>36.273636</td>\n",
       "      <td>714074.0</td>\n",
       "      <td>2146245</td>\n",
       "      <td>15094.370184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>117388.1124</td>\n",
       "      <td>251</td>\n",
       "      <td>35.170909</td>\n",
       "      <td>731705.0</td>\n",
       "      <td>2206268</td>\n",
       "      <td>15347.508312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>116289.915163</td>\n",
       "      <td>306</td>\n",
       "      <td>33.953636</td>\n",
       "      <td>833549.0</td>\n",
       "      <td>2371412</td>\n",
       "      <td>6698.22048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>129531.275234</td>\n",
       "      <td>345</td>\n",
       "      <td>35.293636</td>\n",
       "      <td>970665.0</td>\n",
       "      <td>2560330</td>\n",
       "      <td>7087.11424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>132301.357</td>\n",
       "      <td>350</td>\n",
       "      <td>35.749091</td>\n",
       "      <td>969727.0</td>\n",
       "      <td>2779628</td>\n",
       "      <td>4717.215152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         本地生产总值(亿元)  博物馆数量(所)  工业增加值占GDP比重(%)  专利申请数(项) 高等学校在校生数(人)  \\\n",
       "2011      64345.778       135       40.270000  171848.0     1615973   \n",
       "2012     69786.0328       141       39.264545  203514.0     1699817   \n",
       "2013      76850.877       168       39.113636  232437.0     1775951   \n",
       "2014       82583.13       178       39.070000  249685.0     1853976   \n",
       "2015      87842.928       186       38.500000  319705.0     1957679   \n",
       "2016     94353.4676       186       37.457273  453143.0     1999748   \n",
       "2017    103061.1102       187       36.829091  565786.0     2107745   \n",
       "2018    110536.4098       188       36.273636  714074.0     2146245   \n",
       "2019    117388.1124       251       35.170909  731705.0     2206268   \n",
       "2020  116289.915163       306       33.953636  833549.0     2371412   \n",
       "2021  129531.275234       345       35.293636  970665.0     2560330   \n",
       "2022     132301.357       350       35.749091  969727.0     2779628   \n",
       "\n",
       "          旅游收入(亿元)  \n",
       "2011   8326.387288  \n",
       "2012   9698.407784  \n",
       "2013  10972.355544  \n",
       "2014  12391.391136  \n",
       "2015  11893.091864  \n",
       "2016  12184.537432  \n",
       "2017   13519.65236  \n",
       "2018  15094.370184  \n",
       "2019  15347.508312  \n",
       "2020    6698.22048  \n",
       "2021    7087.11424  \n",
       "2022   4717.215152  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并数据\n",
    "years = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\n",
    "df = pd.DataFrame({\n",
    "    '本地生产总值(亿元)': data1['粤港澳大湾区'][7:].reindex(years)\n",
    "}, index=years)\n",
    "df['博物馆数量(所)'],df['工业增加值占GDP比重(%)'] = data8['粤港澳'][:-1].values, data9['粤港澳'][1:].values\n",
    "df['专利申请数(项)'], df['高等学校在校生数(人)'] = data10['粤港澳'][3:].values, data12['粤港澳'][:-1].values\n",
    "df['旅游收入(亿元)'] = data13['粤港澳'].values\n",
    "df\n",
    "#df.to_excel('粤港澳大湾区经济指标.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617b293f",
   "metadata": {},
   "source": [
    "### x13 产业的数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2da2abee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>第一产业产值（万亿元人民币）</th>\n",
       "      <th>第二产业产值（万亿元人民币）</th>\n",
       "      <th>第三产业产值（万亿元人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.800</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.916</td>\n",
       "      <td>1.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1.080</td>\n",
       "      <td>1.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.338</td>\n",
       "      <td>1.352</td>\n",
       "      <td>1.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.350</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.420</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.516</td>\n",
       "      <td>2.064</td>\n",
       "      <td>2.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.612</td>\n",
       "      <td>2.448</td>\n",
       "      <td>3.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.692</td>\n",
       "      <td>3.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.744</td>\n",
       "      <td>2.976</td>\n",
       "      <td>3.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.805</td>\n",
       "      <td>3.220</td>\n",
       "      <td>4.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.861</td>\n",
       "      <td>3.444</td>\n",
       "      <td>4.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.933</td>\n",
       "      <td>3.732</td>\n",
       "      <td>4.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.026</td>\n",
       "      <td>4.104</td>\n",
       "      <td>5.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.500</td>\n",
       "      <td>5.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>1.238</td>\n",
       "      <td>4.952</td>\n",
       "      <td>6.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>1.200</td>\n",
       "      <td>4.800</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>1.430</td>\n",
       "      <td>5.720</td>\n",
       "      <td>7.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023</td>\n",
       "      <td>1.512</td>\n",
       "      <td>6.048</td>\n",
       "      <td>7.560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      年份  第一产业产值（万亿元人民币）  第二产业产值（万亿元人民币）  第三产业产值（万亿元人民币）\n",
       "0   2000           0.124           0.496           0.620\n",
       "1   2001           0.137           0.548           0.685\n",
       "2   2002           0.152           0.608           0.760\n",
       "3   2003           0.172           0.688           0.860\n",
       "4   2004           0.200           0.800           1.000\n",
       "5   2005           0.229           0.916           1.145\n",
       "6   2006           0.270           1.080           1.350\n",
       "7   2007           0.338           1.352           1.690\n",
       "8   2008           0.350           1.500           1.800\n",
       "9   2009           0.420           1.600           1.900\n",
       "10  2010           0.516           2.064           2.580\n",
       "11  2011           0.612           2.448           3.060\n",
       "12  2012           0.673           2.692           3.365\n",
       "13  2013           0.744           2.976           3.720\n",
       "14  2014           0.805           3.220           4.025\n",
       "15  2015           0.861           3.444           4.305\n",
       "16  2016           0.933           3.732           4.665\n",
       "17  2017           1.026           4.104           5.130\n",
       "18  2018           1.125           4.500           5.625\n",
       "19  2019           1.238           4.952           6.190\n",
       "20  2020           1.200           4.800           6.000\n",
       "21  2021           1.250           5.000           6.200\n",
       "22  2022           1.430           5.720           7.150\n",
       "23  2023           1.512           6.048           7.560"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data14 = pd.read_excel(r\"粤港澳产业.xlsx\")\n",
    "data14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41039874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>第一产业产值（万亿元人民币）</th>\n",
       "      <th>第二产业产值（万亿元人民币）</th>\n",
       "      <th>第三产业产值（万亿元人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.916</td>\n",
       "      <td>1.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1.080</td>\n",
       "      <td>1.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.338</td>\n",
       "      <td>1.352</td>\n",
       "      <td>1.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.350</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.420</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.516</td>\n",
       "      <td>2.064</td>\n",
       "      <td>2.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.612</td>\n",
       "      <td>2.448</td>\n",
       "      <td>3.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.692</td>\n",
       "      <td>3.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.744</td>\n",
       "      <td>2.976</td>\n",
       "      <td>3.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.805</td>\n",
       "      <td>3.220</td>\n",
       "      <td>4.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.861</td>\n",
       "      <td>3.444</td>\n",
       "      <td>4.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.933</td>\n",
       "      <td>3.732</td>\n",
       "      <td>4.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.026</td>\n",
       "      <td>4.104</td>\n",
       "      <td>5.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.500</td>\n",
       "      <td>5.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>1.238</td>\n",
       "      <td>4.952</td>\n",
       "      <td>6.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>1.200</td>\n",
       "      <td>4.800</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>1.430</td>\n",
       "      <td>5.720</td>\n",
       "      <td>7.150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      年份  第一产业产值（万亿元人民币）  第二产业产值（万亿元人民币）  第三产业产值（万亿元人民币）\n",
       "5   2005           0.229           0.916           1.145\n",
       "6   2006           0.270           1.080           1.350\n",
       "7   2007           0.338           1.352           1.690\n",
       "8   2008           0.350           1.500           1.800\n",
       "9   2009           0.420           1.600           1.900\n",
       "10  2010           0.516           2.064           2.580\n",
       "11  2011           0.612           2.448           3.060\n",
       "12  2012           0.673           2.692           3.365\n",
       "13  2013           0.744           2.976           3.720\n",
       "14  2014           0.805           3.220           4.025\n",
       "15  2015           0.861           3.444           4.305\n",
       "16  2016           0.933           3.732           4.665\n",
       "17  2017           1.026           4.104           5.130\n",
       "18  2018           1.125           4.500           5.625\n",
       "19  2019           1.238           4.952           6.190\n",
       "20  2020           1.200           4.800           6.000\n",
       "21  2021           1.250           5.000           6.200\n",
       "22  2022           1.430           5.720           7.150"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data14 = data14[5:-1]\n",
    "data14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b449a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>本地生产总值（亿人民币）</th>\n",
       "      <th>货物进出口货值（亿人民币）</th>\n",
       "      <th>就业人口</th>\n",
       "      <th>零售业销售额</th>\n",
       "      <th>留宿旅客</th>\n",
       "      <th>人均本地生产总值</th>\n",
       "      <th>人口</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>32294.915</td>\n",
       "      <td>71751.468653</td>\n",
       "      <td>3180.01</td>\n",
       "      <td>7976.3634</td>\n",
       "      <td>9859.49</td>\n",
       "      <td>703154.66</td>\n",
       "      <td>5279.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>36802.9652</td>\n",
       "      <td>83104.356700</td>\n",
       "      <td>3358.84</td>\n",
       "      <td>8781.3794</td>\n",
       "      <td>10856.81</td>\n",
       "      <td>795640.1</td>\n",
       "      <td>5476.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>42542.4842</td>\n",
       "      <td>94841.620100</td>\n",
       "      <td>3484.3724</td>\n",
       "      <td>10302.435</td>\n",
       "      <td>12436.52</td>\n",
       "      <td>909349.42</td>\n",
       "      <td>5677.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>47438.9014</td>\n",
       "      <td>100926.005600</td>\n",
       "      <td>3614.9962</td>\n",
       "      <td>12227.0482</td>\n",
       "      <td>13263.7589</td>\n",
       "      <td>991177.02</td>\n",
       "      <td>5889.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>49294.331</td>\n",
       "      <td>89568.985100</td>\n",
       "      <td>3790.0489</td>\n",
       "      <td>13563.5424</td>\n",
       "      <td>14515.1542</td>\n",
       "      <td>1003504.7</td>\n",
       "      <td>6114.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>56404.8874</td>\n",
       "      <td>112721.047402</td>\n",
       "      <td>4060.74</td>\n",
       "      <td>16326.6052</td>\n",
       "      <td>16490.3211</td>\n",
       "      <td>1170162.54</td>\n",
       "      <td>6382.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>64345.778</td>\n",
       "      <td>128004.252439</td>\n",
       "      <td>4020.6155</td>\n",
       "      <td>19240.2764</td>\n",
       "      <td>18081.1092</td>\n",
       "      <td>1352198.58</td>\n",
       "      <td>6704.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>69786.0328</td>\n",
       "      <td>135503.368400</td>\n",
       "      <td>4038.97</td>\n",
       "      <td>21379.6446</td>\n",
       "      <td>19481.985</td>\n",
       "      <td>1444886.08</td>\n",
       "      <td>6991.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>76850.877</td>\n",
       "      <td>145539.477970</td>\n",
       "      <td>4431.94</td>\n",
       "      <td>23780.7502</td>\n",
       "      <td>20680.5287</td>\n",
       "      <td>1575583.3</td>\n",
       "      <td>7228.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>82583.13</td>\n",
       "      <td>146837.260606</td>\n",
       "      <td>4621.28</td>\n",
       "      <td>26403.0952</td>\n",
       "      <td>21797.9201</td>\n",
       "      <td>1632548.28</td>\n",
       "      <td>7453.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>87842.928</td>\n",
       "      <td>140761.156012</td>\n",
       "      <td>4760.59</td>\n",
       "      <td>28336.8742</td>\n",
       "      <td>23334.0354</td>\n",
       "      <td>1550925.18</td>\n",
       "      <td>7664.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>94353.4676</td>\n",
       "      <td>135402.490350</td>\n",
       "      <td>4936.77</td>\n",
       "      <td>30295.0526</td>\n",
       "      <td>25180.3679</td>\n",
       "      <td>1600055.12</td>\n",
       "      <td>7903.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>103061.1102</td>\n",
       "      <td>144625.151725</td>\n",
       "      <td>5109.06</td>\n",
       "      <td>33036.2692</td>\n",
       "      <td>27355.51</td>\n",
       "      <td>1734214.74</td>\n",
       "      <td>8144.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>110536.4098</td>\n",
       "      <td>155761.124400</td>\n",
       "      <td>5296.72</td>\n",
       "      <td>35951.6778</td>\n",
       "      <td>29307.57</td>\n",
       "      <td>1832854.54</td>\n",
       "      <td>8360.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>117388.1124</td>\n",
       "      <td>147973.791592</td>\n",
       "      <td>5261.98</td>\n",
       "      <td>37898.965</td>\n",
       "      <td>31108.18</td>\n",
       "      <td>1856018.54</td>\n",
       "      <td>8503.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>116289.915163</td>\n",
       "      <td>145991.088605</td>\n",
       "      <td>5319.75</td>\n",
       "      <td>34622.4342</td>\n",
       "      <td>17201.63</td>\n",
       "      <td>1512482.401966</td>\n",
       "      <td>8634.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>129531.275234</td>\n",
       "      <td>182762.025515</td>\n",
       "      <td>5354.22</td>\n",
       "      <td>38326.69813</td>\n",
       "      <td>20644.63</td>\n",
       "      <td>1699615.754954</td>\n",
       "      <td>8669.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>132301.357</td>\n",
       "      <td>172356.594545</td>\n",
       "      <td>5161.48</td>\n",
       "      <td>38698.5328</td>\n",
       "      <td>16235.79</td>\n",
       "      <td>1668031.040905</td>\n",
       "      <td>8643.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       本地生产总值（亿人民币）  货物进出口货值（亿人民币）       就业人口       零售业销售额        留宿旅客  \\\n",
       "年                                                                        \n",
       "2005      32294.915   71751.468653    3180.01    7976.3634     9859.49   \n",
       "2006     36802.9652   83104.356700    3358.84    8781.3794    10856.81   \n",
       "2007     42542.4842   94841.620100  3484.3724    10302.435    12436.52   \n",
       "2008     47438.9014  100926.005600  3614.9962   12227.0482  13263.7589   \n",
       "2009      49294.331   89568.985100  3790.0489   13563.5424  14515.1542   \n",
       "2010     56404.8874  112721.047402    4060.74   16326.6052  16490.3211   \n",
       "2011      64345.778  128004.252439  4020.6155   19240.2764  18081.1092   \n",
       "2012     69786.0328  135503.368400    4038.97   21379.6446   19481.985   \n",
       "2013      76850.877  145539.477970    4431.94   23780.7502  20680.5287   \n",
       "2014       82583.13  146837.260606    4621.28   26403.0952  21797.9201   \n",
       "2015      87842.928  140761.156012    4760.59   28336.8742  23334.0354   \n",
       "2016     94353.4676  135402.490350    4936.77   30295.0526  25180.3679   \n",
       "2017    103061.1102  144625.151725    5109.06   33036.2692    27355.51   \n",
       "2018    110536.4098  155761.124400    5296.72   35951.6778    29307.57   \n",
       "2019    117388.1124  147973.791592    5261.98    37898.965    31108.18   \n",
       "2020  116289.915163  145991.088605    5319.75   34622.4342    17201.63   \n",
       "2021  129531.275234  182762.025515    5354.22  38326.69813    20644.63   \n",
       "2022     132301.357  172356.594545    5161.48   38698.5328    16235.79   \n",
       "\n",
       "            人均本地生产总值       人口  \n",
       "年                              \n",
       "2005       703154.66  5279.35  \n",
       "2006        795640.1  5476.89  \n",
       "2007       909349.42   5677.7  \n",
       "2008       991177.02  5889.18  \n",
       "2009       1003504.7   6114.7  \n",
       "2010      1170162.54  6382.22  \n",
       "2011      1352198.58  6704.63  \n",
       "2012      1444886.08   6991.9  \n",
       "2013       1575583.3   7228.7  \n",
       "2014      1632548.28  7453.77  \n",
       "2015      1550925.18  7664.16  \n",
       "2016      1600055.12  7903.54  \n",
       "2017      1734214.74  8144.86  \n",
       "2018      1832854.54  8360.97  \n",
       "2019      1856018.54  8503.96  \n",
       "2020  1512482.401966  8634.52  \n",
       "2021  1699615.754954  8669.07  \n",
       "2022  1668031.040905  8643.97  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取粤港澳大湾区列\n",
    "data1 = data1['粤港澳大湾区']\n",
    "data2 = data2['粤港澳大湾区']\n",
    "data3 = data3['粤港澳大湾区']\n",
    "data4 = data4['粤港澳大湾区']\n",
    "data5 = data5['粤港澳大湾区']\n",
    "data6 = data6['粤港澳大湾区']\n",
    "data7 = data7['粤港澳大湾区']\n",
    "\n",
    "# 创建新的DataFrame\n",
    "data = pd.DataFrame({\n",
    "    '本地生产总值（亿人民币）': data1,\n",
    "    '货物进出口货值（亿人民币）': data2,\n",
    "    '就业人口': data3,\n",
    "    '零售业销售额': data4,\n",
    "    '留宿旅客': data5,\n",
    "    '人均本地生产总值': data6,\n",
    "    '人口': data7\n",
    "})\n",
    "data = data[6:-1]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f4f1db2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>本地生产总值（亿人民币）</th>\n",
       "      <th>货物进出口货值（亿人民币）</th>\n",
       "      <th>就业人口</th>\n",
       "      <th>零售业销售额</th>\n",
       "      <th>留宿旅客</th>\n",
       "      <th>人均本地生产总值</th>\n",
       "      <th>人口</th>\n",
       "      <th>第一产业产值（万亿元人民币）</th>\n",
       "      <th>第二产业产值（万亿元人民币）</th>\n",
       "      <th>第三产业产值（万亿元人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>32294.915</td>\n",
       "      <td>71751.468653</td>\n",
       "      <td>3180.01</td>\n",
       "      <td>7976.3634</td>\n",
       "      <td>9859.49</td>\n",
       "      <td>703154.66</td>\n",
       "      <td>5279.35</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.916</td>\n",
       "      <td>1.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>36802.9652</td>\n",
       "      <td>83104.356700</td>\n",
       "      <td>3358.84</td>\n",
       "      <td>8781.3794</td>\n",
       "      <td>10856.81</td>\n",
       "      <td>795640.1</td>\n",
       "      <td>5476.89</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1.080</td>\n",
       "      <td>1.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>42542.4842</td>\n",
       "      <td>94841.620100</td>\n",
       "      <td>3484.3724</td>\n",
       "      <td>10302.435</td>\n",
       "      <td>12436.52</td>\n",
       "      <td>909349.42</td>\n",
       "      <td>5677.7</td>\n",
       "      <td>0.338</td>\n",
       "      <td>1.352</td>\n",
       "      <td>1.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>47438.9014</td>\n",
       "      <td>100926.005600</td>\n",
       "      <td>3614.9962</td>\n",
       "      <td>12227.0482</td>\n",
       "      <td>13263.7589</td>\n",
       "      <td>991177.02</td>\n",
       "      <td>5889.18</td>\n",
       "      <td>0.350</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>49294.331</td>\n",
       "      <td>89568.985100</td>\n",
       "      <td>3790.0489</td>\n",
       "      <td>13563.5424</td>\n",
       "      <td>14515.1542</td>\n",
       "      <td>1003504.7</td>\n",
       "      <td>6114.7</td>\n",
       "      <td>0.420</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>56404.8874</td>\n",
       "      <td>112721.047402</td>\n",
       "      <td>4060.74</td>\n",
       "      <td>16326.6052</td>\n",
       "      <td>16490.3211</td>\n",
       "      <td>1170162.54</td>\n",
       "      <td>6382.22</td>\n",
       "      <td>0.516</td>\n",
       "      <td>2.064</td>\n",
       "      <td>2.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>64345.778</td>\n",
       "      <td>128004.252439</td>\n",
       "      <td>4020.6155</td>\n",
       "      <td>19240.2764</td>\n",
       "      <td>18081.1092</td>\n",
       "      <td>1352198.58</td>\n",
       "      <td>6704.63</td>\n",
       "      <td>0.612</td>\n",
       "      <td>2.448</td>\n",
       "      <td>3.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>69786.0328</td>\n",
       "      <td>135503.368400</td>\n",
       "      <td>4038.97</td>\n",
       "      <td>21379.6446</td>\n",
       "      <td>19481.985</td>\n",
       "      <td>1444886.08</td>\n",
       "      <td>6991.9</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.692</td>\n",
       "      <td>3.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>76850.877</td>\n",
       "      <td>145539.477970</td>\n",
       "      <td>4431.94</td>\n",
       "      <td>23780.7502</td>\n",
       "      <td>20680.5287</td>\n",
       "      <td>1575583.3</td>\n",
       "      <td>7228.7</td>\n",
       "      <td>0.744</td>\n",
       "      <td>2.976</td>\n",
       "      <td>3.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>82583.13</td>\n",
       "      <td>146837.260606</td>\n",
       "      <td>4621.28</td>\n",
       "      <td>26403.0952</td>\n",
       "      <td>21797.9201</td>\n",
       "      <td>1632548.28</td>\n",
       "      <td>7453.77</td>\n",
       "      <td>0.805</td>\n",
       "      <td>3.220</td>\n",
       "      <td>4.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>87842.928</td>\n",
       "      <td>140761.156012</td>\n",
       "      <td>4760.59</td>\n",
       "      <td>28336.8742</td>\n",
       "      <td>23334.0354</td>\n",
       "      <td>1550925.18</td>\n",
       "      <td>7664.16</td>\n",
       "      <td>0.861</td>\n",
       "      <td>3.444</td>\n",
       "      <td>4.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>94353.4676</td>\n",
       "      <td>135402.490350</td>\n",
       "      <td>4936.77</td>\n",
       "      <td>30295.0526</td>\n",
       "      <td>25180.3679</td>\n",
       "      <td>1600055.12</td>\n",
       "      <td>7903.54</td>\n",
       "      <td>0.933</td>\n",
       "      <td>3.732</td>\n",
       "      <td>4.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>103061.1102</td>\n",
       "      <td>144625.151725</td>\n",
       "      <td>5109.06</td>\n",
       "      <td>33036.2692</td>\n",
       "      <td>27355.51</td>\n",
       "      <td>1734214.74</td>\n",
       "      <td>8144.86</td>\n",
       "      <td>1.026</td>\n",
       "      <td>4.104</td>\n",
       "      <td>5.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>110536.4098</td>\n",
       "      <td>155761.124400</td>\n",
       "      <td>5296.72</td>\n",
       "      <td>35951.6778</td>\n",
       "      <td>29307.57</td>\n",
       "      <td>1832854.54</td>\n",
       "      <td>8360.97</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.500</td>\n",
       "      <td>5.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>117388.1124</td>\n",
       "      <td>147973.791592</td>\n",
       "      <td>5261.98</td>\n",
       "      <td>37898.965</td>\n",
       "      <td>31108.18</td>\n",
       "      <td>1856018.54</td>\n",
       "      <td>8503.96</td>\n",
       "      <td>1.238</td>\n",
       "      <td>4.952</td>\n",
       "      <td>6.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>116289.915163</td>\n",
       "      <td>145991.088605</td>\n",
       "      <td>5319.75</td>\n",
       "      <td>34622.4342</td>\n",
       "      <td>17201.63</td>\n",
       "      <td>1512482.401966</td>\n",
       "      <td>8634.52</td>\n",
       "      <td>1.200</td>\n",
       "      <td>4.800</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>129531.275234</td>\n",
       "      <td>182762.025515</td>\n",
       "      <td>5354.22</td>\n",
       "      <td>38326.69813</td>\n",
       "      <td>20644.63</td>\n",
       "      <td>1699615.754954</td>\n",
       "      <td>8669.07</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>132301.357</td>\n",
       "      <td>172356.594545</td>\n",
       "      <td>5161.48</td>\n",
       "      <td>38698.5328</td>\n",
       "      <td>16235.79</td>\n",
       "      <td>1668031.040905</td>\n",
       "      <td>8643.97</td>\n",
       "      <td>1.430</td>\n",
       "      <td>5.720</td>\n",
       "      <td>7.150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       本地生产总值（亿人民币）  货物进出口货值（亿人民币）       就业人口       零售业销售额        留宿旅客  \\\n",
       "2005      32294.915   71751.468653    3180.01    7976.3634     9859.49   \n",
       "2006     36802.9652   83104.356700    3358.84    8781.3794    10856.81   \n",
       "2007     42542.4842   94841.620100  3484.3724    10302.435    12436.52   \n",
       "2008     47438.9014  100926.005600  3614.9962   12227.0482  13263.7589   \n",
       "2009      49294.331   89568.985100  3790.0489   13563.5424  14515.1542   \n",
       "2010     56404.8874  112721.047402    4060.74   16326.6052  16490.3211   \n",
       "2011      64345.778  128004.252439  4020.6155   19240.2764  18081.1092   \n",
       "2012     69786.0328  135503.368400    4038.97   21379.6446   19481.985   \n",
       "2013      76850.877  145539.477970    4431.94   23780.7502  20680.5287   \n",
       "2014       82583.13  146837.260606    4621.28   26403.0952  21797.9201   \n",
       "2015      87842.928  140761.156012    4760.59   28336.8742  23334.0354   \n",
       "2016     94353.4676  135402.490350    4936.77   30295.0526  25180.3679   \n",
       "2017    103061.1102  144625.151725    5109.06   33036.2692    27355.51   \n",
       "2018    110536.4098  155761.124400    5296.72   35951.6778    29307.57   \n",
       "2019    117388.1124  147973.791592    5261.98    37898.965    31108.18   \n",
       "2020  116289.915163  145991.088605    5319.75   34622.4342    17201.63   \n",
       "2021  129531.275234  182762.025515    5354.22  38326.69813    20644.63   \n",
       "2022     132301.357  172356.594545    5161.48   38698.5328    16235.79   \n",
       "\n",
       "            人均本地生产总值       人口  第一产业产值（万亿元人民币）  第二产业产值（万亿元人民币）  第三产业产值（万亿元人民币）  \n",
       "2005       703154.66  5279.35           0.229           0.916           1.145  \n",
       "2006        795640.1  5476.89           0.270           1.080           1.350  \n",
       "2007       909349.42   5677.7           0.338           1.352           1.690  \n",
       "2008       991177.02  5889.18           0.350           1.500           1.800  \n",
       "2009       1003504.7   6114.7           0.420           1.600           1.900  \n",
       "2010      1170162.54  6382.22           0.516           2.064           2.580  \n",
       "2011      1352198.58  6704.63           0.612           2.448           3.060  \n",
       "2012      1444886.08   6991.9           0.673           2.692           3.365  \n",
       "2013       1575583.3   7228.7           0.744           2.976           3.720  \n",
       "2014      1632548.28  7453.77           0.805           3.220           4.025  \n",
       "2015      1550925.18  7664.16           0.861           3.444           4.305  \n",
       "2016      1600055.12  7903.54           0.933           3.732           4.665  \n",
       "2017      1734214.74  8144.86           1.026           4.104           5.130  \n",
       "2018      1832854.54  8360.97           1.125           4.500           5.625  \n",
       "2019      1856018.54  8503.96           1.238           4.952           6.190  \n",
       "2020  1512482.401966  8634.52           1.200           4.800           6.000  \n",
       "2021  1699615.754954  8669.07           1.250           5.000           6.200  \n",
       "2022  1668031.040905  8643.97           1.430           5.720           7.150  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data14.set_index('年份', inplace=True)\n",
    "data = pd.concat([data, data14], axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d527415b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>本地生产总值（亿人民币）</th>\n",
       "      <th>货物进出口货值（亿人民币）</th>\n",
       "      <th>就业人口</th>\n",
       "      <th>零售业销售额</th>\n",
       "      <th>留宿旅客</th>\n",
       "      <th>人均本地生产总值</th>\n",
       "      <th>人口</th>\n",
       "      <th>第一产业产值（万亿元人民币）</th>\n",
       "      <th>第二产业产值（万亿元人民币）</th>\n",
       "      <th>第三产业产值（万亿元人民币）</th>\n",
       "      <th>本地生产总值(亿元)</th>\n",
       "      <th>博物馆数量(所)</th>\n",
       "      <th>工业增加值占GDP比重(%)</th>\n",
       "      <th>专利申请数(项)</th>\n",
       "      <th>高等学校在校生数(人)</th>\n",
       "      <th>旅游收入(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>64345.778</td>\n",
       "      <td>128004.252439</td>\n",
       "      <td>4020.6155</td>\n",
       "      <td>19240.2764</td>\n",
       "      <td>18081.1092</td>\n",
       "      <td>1352198.58</td>\n",
       "      <td>6704.63</td>\n",
       "      <td>0.612</td>\n",
       "      <td>2.448</td>\n",
       "      <td>3.060</td>\n",
       "      <td>64345.778</td>\n",
       "      <td>135</td>\n",
       "      <td>40.270000</td>\n",
       "      <td>171848.0</td>\n",
       "      <td>1615973</td>\n",
       "      <td>8326.387288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>69786.0328</td>\n",
       "      <td>135503.368400</td>\n",
       "      <td>4038.97</td>\n",
       "      <td>21379.6446</td>\n",
       "      <td>19481.985</td>\n",
       "      <td>1444886.08</td>\n",
       "      <td>6991.9</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.692</td>\n",
       "      <td>3.365</td>\n",
       "      <td>69786.0328</td>\n",
       "      <td>141</td>\n",
       "      <td>39.264545</td>\n",
       "      <td>203514.0</td>\n",
       "      <td>1699817</td>\n",
       "      <td>9698.407784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>76850.877</td>\n",
       "      <td>145539.477970</td>\n",
       "      <td>4431.94</td>\n",
       "      <td>23780.7502</td>\n",
       "      <td>20680.5287</td>\n",
       "      <td>1575583.3</td>\n",
       "      <td>7228.7</td>\n",
       "      <td>0.744</td>\n",
       "      <td>2.976</td>\n",
       "      <td>3.720</td>\n",
       "      <td>76850.877</td>\n",
       "      <td>168</td>\n",
       "      <td>39.113636</td>\n",
       "      <td>232437.0</td>\n",
       "      <td>1775951</td>\n",
       "      <td>10972.355544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>82583.13</td>\n",
       "      <td>146837.260606</td>\n",
       "      <td>4621.28</td>\n",
       "      <td>26403.0952</td>\n",
       "      <td>21797.9201</td>\n",
       "      <td>1632548.28</td>\n",
       "      <td>7453.77</td>\n",
       "      <td>0.805</td>\n",
       "      <td>3.220</td>\n",
       "      <td>4.025</td>\n",
       "      <td>82583.13</td>\n",
       "      <td>178</td>\n",
       "      <td>39.070000</td>\n",
       "      <td>249685.0</td>\n",
       "      <td>1853976</td>\n",
       "      <td>12391.391136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>87842.928</td>\n",
       "      <td>140761.156012</td>\n",
       "      <td>4760.59</td>\n",
       "      <td>28336.8742</td>\n",
       "      <td>23334.0354</td>\n",
       "      <td>1550925.18</td>\n",
       "      <td>7664.16</td>\n",
       "      <td>0.861</td>\n",
       "      <td>3.444</td>\n",
       "      <td>4.305</td>\n",
       "      <td>87842.928</td>\n",
       "      <td>186</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>319705.0</td>\n",
       "      <td>1957679</td>\n",
       "      <td>11893.091864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>94353.4676</td>\n",
       "      <td>135402.490350</td>\n",
       "      <td>4936.77</td>\n",
       "      <td>30295.0526</td>\n",
       "      <td>25180.3679</td>\n",
       "      <td>1600055.12</td>\n",
       "      <td>7903.54</td>\n",
       "      <td>0.933</td>\n",
       "      <td>3.732</td>\n",
       "      <td>4.665</td>\n",
       "      <td>94353.4676</td>\n",
       "      <td>186</td>\n",
       "      <td>37.457273</td>\n",
       "      <td>453143.0</td>\n",
       "      <td>1999748</td>\n",
       "      <td>12184.537432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>103061.1102</td>\n",
       "      <td>144625.151725</td>\n",
       "      <td>5109.06</td>\n",
       "      <td>33036.2692</td>\n",
       "      <td>27355.51</td>\n",
       "      <td>1734214.74</td>\n",
       "      <td>8144.86</td>\n",
       "      <td>1.026</td>\n",
       "      <td>4.104</td>\n",
       "      <td>5.130</td>\n",
       "      <td>103061.1102</td>\n",
       "      <td>187</td>\n",
       "      <td>36.829091</td>\n",
       "      <td>565786.0</td>\n",
       "      <td>2107745</td>\n",
       "      <td>13519.65236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>110536.4098</td>\n",
       "      <td>155761.124400</td>\n",
       "      <td>5296.72</td>\n",
       "      <td>35951.6778</td>\n",
       "      <td>29307.57</td>\n",
       "      <td>1832854.54</td>\n",
       "      <td>8360.97</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.500</td>\n",
       "      <td>5.625</td>\n",
       "      <td>110536.4098</td>\n",
       "      <td>188</td>\n",
       "      <td>36.273636</td>\n",
       "      <td>714074.0</td>\n",
       "      <td>2146245</td>\n",
       "      <td>15094.370184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>117388.1124</td>\n",
       "      <td>147973.791592</td>\n",
       "      <td>5261.98</td>\n",
       "      <td>37898.965</td>\n",
       "      <td>31108.18</td>\n",
       "      <td>1856018.54</td>\n",
       "      <td>8503.96</td>\n",
       "      <td>1.238</td>\n",
       "      <td>4.952</td>\n",
       "      <td>6.190</td>\n",
       "      <td>117388.1124</td>\n",
       "      <td>251</td>\n",
       "      <td>35.170909</td>\n",
       "      <td>731705.0</td>\n",
       "      <td>2206268</td>\n",
       "      <td>15347.508312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>116289.915163</td>\n",
       "      <td>145991.088605</td>\n",
       "      <td>5319.75</td>\n",
       "      <td>34622.4342</td>\n",
       "      <td>17201.63</td>\n",
       "      <td>1512482.401966</td>\n",
       "      <td>8634.52</td>\n",
       "      <td>1.200</td>\n",
       "      <td>4.800</td>\n",
       "      <td>6.000</td>\n",
       "      <td>116289.915163</td>\n",
       "      <td>306</td>\n",
       "      <td>33.953636</td>\n",
       "      <td>833549.0</td>\n",
       "      <td>2371412</td>\n",
       "      <td>6698.22048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>129531.275234</td>\n",
       "      <td>182762.025515</td>\n",
       "      <td>5354.22</td>\n",
       "      <td>38326.69813</td>\n",
       "      <td>20644.63</td>\n",
       "      <td>1699615.754954</td>\n",
       "      <td>8669.07</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.200</td>\n",
       "      <td>129531.275234</td>\n",
       "      <td>345</td>\n",
       "      <td>35.293636</td>\n",
       "      <td>970665.0</td>\n",
       "      <td>2560330</td>\n",
       "      <td>7087.11424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>132301.357</td>\n",
       "      <td>172356.594545</td>\n",
       "      <td>5161.48</td>\n",
       "      <td>38698.5328</td>\n",
       "      <td>16235.79</td>\n",
       "      <td>1668031.040905</td>\n",
       "      <td>8643.97</td>\n",
       "      <td>1.430</td>\n",
       "      <td>5.720</td>\n",
       "      <td>7.150</td>\n",
       "      <td>132301.357</td>\n",
       "      <td>350</td>\n",
       "      <td>35.749091</td>\n",
       "      <td>969727.0</td>\n",
       "      <td>2779628</td>\n",
       "      <td>4717.215152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       本地生产总值（亿人民币）  货物进出口货值（亿人民币）       就业人口       零售业销售额        留宿旅客  \\\n",
       "2011      64345.778  128004.252439  4020.6155   19240.2764  18081.1092   \n",
       "2012     69786.0328  135503.368400    4038.97   21379.6446   19481.985   \n",
       "2013      76850.877  145539.477970    4431.94   23780.7502  20680.5287   \n",
       "2014       82583.13  146837.260606    4621.28   26403.0952  21797.9201   \n",
       "2015      87842.928  140761.156012    4760.59   28336.8742  23334.0354   \n",
       "2016     94353.4676  135402.490350    4936.77   30295.0526  25180.3679   \n",
       "2017    103061.1102  144625.151725    5109.06   33036.2692    27355.51   \n",
       "2018    110536.4098  155761.124400    5296.72   35951.6778    29307.57   \n",
       "2019    117388.1124  147973.791592    5261.98    37898.965    31108.18   \n",
       "2020  116289.915163  145991.088605    5319.75   34622.4342    17201.63   \n",
       "2021  129531.275234  182762.025515    5354.22  38326.69813    20644.63   \n",
       "2022     132301.357  172356.594545    5161.48   38698.5328    16235.79   \n",
       "\n",
       "            人均本地生产总值       人口  第一产业产值（万亿元人民币）  第二产业产值（万亿元人民币）  第三产业产值（万亿元人民币）  \\\n",
       "2011      1352198.58  6704.63           0.612           2.448           3.060   \n",
       "2012      1444886.08   6991.9           0.673           2.692           3.365   \n",
       "2013       1575583.3   7228.7           0.744           2.976           3.720   \n",
       "2014      1632548.28  7453.77           0.805           3.220           4.025   \n",
       "2015      1550925.18  7664.16           0.861           3.444           4.305   \n",
       "2016      1600055.12  7903.54           0.933           3.732           4.665   \n",
       "2017      1734214.74  8144.86           1.026           4.104           5.130   \n",
       "2018      1832854.54  8360.97           1.125           4.500           5.625   \n",
       "2019      1856018.54  8503.96           1.238           4.952           6.190   \n",
       "2020  1512482.401966  8634.52           1.200           4.800           6.000   \n",
       "2021  1699615.754954  8669.07           1.250           5.000           6.200   \n",
       "2022  1668031.040905  8643.97           1.430           5.720           7.150   \n",
       "\n",
       "         本地生产总值(亿元)  博物馆数量(所)  工业增加值占GDP比重(%)  专利申请数(项) 高等学校在校生数(人)  \\\n",
       "2011      64345.778       135       40.270000  171848.0     1615973   \n",
       "2012     69786.0328       141       39.264545  203514.0     1699817   \n",
       "2013      76850.877       168       39.113636  232437.0     1775951   \n",
       "2014       82583.13       178       39.070000  249685.0     1853976   \n",
       "2015      87842.928       186       38.500000  319705.0     1957679   \n",
       "2016     94353.4676       186       37.457273  453143.0     1999748   \n",
       "2017    103061.1102       187       36.829091  565786.0     2107745   \n",
       "2018    110536.4098       188       36.273636  714074.0     2146245   \n",
       "2019    117388.1124       251       35.170909  731705.0     2206268   \n",
       "2020  116289.915163       306       33.953636  833549.0     2371412   \n",
       "2021  129531.275234       345       35.293636  970665.0     2560330   \n",
       "2022     132301.357       350       35.749091  969727.0     2779628   \n",
       "\n",
       "          旅游收入(亿元)  \n",
       "2011   8326.387288  \n",
       "2012   9698.407784  \n",
       "2013  10972.355544  \n",
       "2014  12391.391136  \n",
       "2015  11893.091864  \n",
       "2016  12184.537432  \n",
       "2017   13519.65236  \n",
       "2018  15094.370184  \n",
       "2019  15347.508312  \n",
       "2020    6698.22048  \n",
       "2021    7087.11424  \n",
       "2022   4717.215152  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.loc[2011:]\n",
    "data = pd.merge(data, df, left_index=True, right_index=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5d03cafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reset_index()\n",
    "data.to_excel('粤港澳大湾区数据.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f21af0",
   "metadata": {},
   "source": [
    "# 第一问"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56657552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>本地生产总值（亿人民币）</th>\n",
       "      <th>货物进出口货值（亿人民币）</th>\n",
       "      <th>就业人口</th>\n",
       "      <th>零售业销售额</th>\n",
       "      <th>留宿旅客</th>\n",
       "      <th>人均本地生产总值</th>\n",
       "      <th>人口</th>\n",
       "      <th>第一产业产值（万亿元人民币）</th>\n",
       "      <th>第二产业产值（万亿元人民币）</th>\n",
       "      <th>第三产业产值（万亿元人民币）</th>\n",
       "      <th>本地生产总值(亿元)</th>\n",
       "      <th>博物馆数量(所)</th>\n",
       "      <th>工业增加值占GDP比重(%)</th>\n",
       "      <th>专利申请数(项)</th>\n",
       "      <th>高等学校在校生数(人)</th>\n",
       "      <th>旅游收入(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>64345.7780</td>\n",
       "      <td>128004.252439</td>\n",
       "      <td>4020.6155</td>\n",
       "      <td>19240.2764</td>\n",
       "      <td>18081.1092</td>\n",
       "      <td>1352198.58</td>\n",
       "      <td>6704.63</td>\n",
       "      <td>0.612</td>\n",
       "      <td>2.448</td>\n",
       "      <td>3.060</td>\n",
       "      <td>64345.7780</td>\n",
       "      <td>135</td>\n",
       "      <td>40.270000</td>\n",
       "      <td>171848</td>\n",
       "      <td>1615973</td>\n",
       "      <td>8326.387288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>69786.0328</td>\n",
       "      <td>135503.368400</td>\n",
       "      <td>4038.9700</td>\n",
       "      <td>21379.6446</td>\n",
       "      <td>19481.9850</td>\n",
       "      <td>1444886.08</td>\n",
       "      <td>6991.90</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.692</td>\n",
       "      <td>3.365</td>\n",
       "      <td>69786.0328</td>\n",
       "      <td>141</td>\n",
       "      <td>39.264545</td>\n",
       "      <td>203514</td>\n",
       "      <td>1699817</td>\n",
       "      <td>9698.407784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>76850.8770</td>\n",
       "      <td>145539.477970</td>\n",
       "      <td>4431.9400</td>\n",
       "      <td>23780.7502</td>\n",
       "      <td>20680.5287</td>\n",
       "      <td>1575583.30</td>\n",
       "      <td>7228.70</td>\n",
       "      <td>0.744</td>\n",
       "      <td>2.976</td>\n",
       "      <td>3.720</td>\n",
       "      <td>76850.8770</td>\n",
       "      <td>168</td>\n",
       "      <td>39.113636</td>\n",
       "      <td>232437</td>\n",
       "      <td>1775951</td>\n",
       "      <td>10972.355544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>82583.1300</td>\n",
       "      <td>146837.260606</td>\n",
       "      <td>4621.2800</td>\n",
       "      <td>26403.0952</td>\n",
       "      <td>21797.9201</td>\n",
       "      <td>1632548.28</td>\n",
       "      <td>7453.77</td>\n",
       "      <td>0.805</td>\n",
       "      <td>3.220</td>\n",
       "      <td>4.025</td>\n",
       "      <td>82583.1300</td>\n",
       "      <td>178</td>\n",
       "      <td>39.070000</td>\n",
       "      <td>249685</td>\n",
       "      <td>1853976</td>\n",
       "      <td>12391.391136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>87842.9280</td>\n",
       "      <td>140761.156012</td>\n",
       "      <td>4760.5900</td>\n",
       "      <td>28336.8742</td>\n",
       "      <td>23334.0354</td>\n",
       "      <td>1550925.18</td>\n",
       "      <td>7664.16</td>\n",
       "      <td>0.861</td>\n",
       "      <td>3.444</td>\n",
       "      <td>4.305</td>\n",
       "      <td>87842.9280</td>\n",
       "      <td>186</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>319705</td>\n",
       "      <td>1957679</td>\n",
       "      <td>11893.091864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  本地生产总值（亿人民币）  货物进出口货值（亿人民币）       就业人口      零售业销售额        留宿旅客  \\\n",
       "0   2011    64345.7780  128004.252439  4020.6155  19240.2764  18081.1092   \n",
       "1   2012    69786.0328  135503.368400  4038.9700  21379.6446  19481.9850   \n",
       "2   2013    76850.8770  145539.477970  4431.9400  23780.7502  20680.5287   \n",
       "3   2014    82583.1300  146837.260606  4621.2800  26403.0952  21797.9201   \n",
       "4   2015    87842.9280  140761.156012  4760.5900  28336.8742  23334.0354   \n",
       "\n",
       "     人均本地生产总值       人口  第一产业产值（万亿元人民币）  第二产业产值（万亿元人民币）  第三产业产值（万亿元人民币）  \\\n",
       "0  1352198.58  6704.63           0.612           2.448           3.060   \n",
       "1  1444886.08  6991.90           0.673           2.692           3.365   \n",
       "2  1575583.30  7228.70           0.744           2.976           3.720   \n",
       "3  1632548.28  7453.77           0.805           3.220           4.025   \n",
       "4  1550925.18  7664.16           0.861           3.444           4.305   \n",
       "\n",
       "   本地生产总值(亿元)  博物馆数量(所)  工业增加值占GDP比重(%)  专利申请数(项)  高等学校在校生数(人)      旅游收入(亿元)  \n",
       "0  64345.7780       135       40.270000    171848      1615973   8326.387288  \n",
       "1  69786.0328       141       39.264545    203514      1699817   9698.407784  \n",
       "2  76850.8770       168       39.113636    232437      1775951  10972.355544  \n",
       "3  82583.1300       178       39.070000    249685      1853976  12391.391136  \n",
       "4  87842.9280       186       38.500000    319705      1957679  11893.091864  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# 读取数据\n",
    "data = pd.read_excel(\"粤港澳大湾区数据.xlsx\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad6989d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>本地生产总值（亿人民币）</th>\n",
       "      <th>货物进出口货值（亿人民币）</th>\n",
       "      <th>就业人口</th>\n",
       "      <th>零售业销售额</th>\n",
       "      <th>留宿旅客</th>\n",
       "      <th>人均本地生产总值</th>\n",
       "      <th>人口</th>\n",
       "      <th>第一产业产值（万亿元人民币）</th>\n",
       "      <th>第二产业产值（万亿元人民币）</th>\n",
       "      <th>第三产业产值（万亿元人民币）</th>\n",
       "      <th>本地生产总值(亿元)</th>\n",
       "      <th>博物馆数量(所)</th>\n",
       "      <th>工业增加值占GDP比重(%)</th>\n",
       "      <th>专利申请数(项)</th>\n",
       "      <th>高等学校在校生数(人)</th>\n",
       "      <th>旅游收入(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.339518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.080056</td>\n",
       "      <td>0.136951</td>\n",
       "      <td>0.013763</td>\n",
       "      <td>0.109947</td>\n",
       "      <td>0.218270</td>\n",
       "      <td>0.183969</td>\n",
       "      <td>0.146235</td>\n",
       "      <td>0.074572</td>\n",
       "      <td>0.074572</td>\n",
       "      <td>0.074572</td>\n",
       "      <td>0.080056</td>\n",
       "      <td>0.027907</td>\n",
       "      <td>0.840818</td>\n",
       "      <td>0.039641</td>\n",
       "      <td>0.072052</td>\n",
       "      <td>0.468585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.184019</td>\n",
       "      <td>0.320233</td>\n",
       "      <td>0.308431</td>\n",
       "      <td>0.233344</td>\n",
       "      <td>0.298858</td>\n",
       "      <td>0.443382</td>\n",
       "      <td>0.266778</td>\n",
       "      <td>0.161369</td>\n",
       "      <td>0.161369</td>\n",
       "      <td>0.161369</td>\n",
       "      <td>0.184019</td>\n",
       "      <td>0.153488</td>\n",
       "      <td>0.816926</td>\n",
       "      <td>0.075848</td>\n",
       "      <td>0.137479</td>\n",
       "      <td>0.588426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.268372</td>\n",
       "      <td>0.343933</td>\n",
       "      <td>0.450407</td>\n",
       "      <td>0.368112</td>\n",
       "      <td>0.373990</td>\n",
       "      <td>0.556448</td>\n",
       "      <td>0.381350</td>\n",
       "      <td>0.235941</td>\n",
       "      <td>0.235941</td>\n",
       "      <td>0.235941</td>\n",
       "      <td>0.268372</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.810017</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>0.204531</td>\n",
       "      <td>0.721916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.345772</td>\n",
       "      <td>0.232970</td>\n",
       "      <td>0.554868</td>\n",
       "      <td>0.467493</td>\n",
       "      <td>0.477277</td>\n",
       "      <td>0.394440</td>\n",
       "      <td>0.488450</td>\n",
       "      <td>0.304401</td>\n",
       "      <td>0.304401</td>\n",
       "      <td>0.304401</td>\n",
       "      <td>0.345772</td>\n",
       "      <td>0.237209</td>\n",
       "      <td>0.719775</td>\n",
       "      <td>0.185095</td>\n",
       "      <td>0.293649</td>\n",
       "      <td>0.675040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.441578</td>\n",
       "      <td>0.135108</td>\n",
       "      <td>0.686976</td>\n",
       "      <td>0.568128</td>\n",
       "      <td>0.601422</td>\n",
       "      <td>0.491955</td>\n",
       "      <td>0.610306</td>\n",
       "      <td>0.392421</td>\n",
       "      <td>0.392421</td>\n",
       "      <td>0.392421</td>\n",
       "      <td>0.441578</td>\n",
       "      <td>0.237209</td>\n",
       "      <td>0.554692</td>\n",
       "      <td>0.352139</td>\n",
       "      <td>0.329801</td>\n",
       "      <td>0.702457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.569715</td>\n",
       "      <td>0.303535</td>\n",
       "      <td>0.816167</td>\n",
       "      <td>0.709005</td>\n",
       "      <td>0.747675</td>\n",
       "      <td>0.758239</td>\n",
       "      <td>0.733150</td>\n",
       "      <td>0.506112</td>\n",
       "      <td>0.506112</td>\n",
       "      <td>0.506112</td>\n",
       "      <td>0.569715</td>\n",
       "      <td>0.241860</td>\n",
       "      <td>0.455239</td>\n",
       "      <td>0.493152</td>\n",
       "      <td>0.422610</td>\n",
       "      <td>0.828052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.679718</td>\n",
       "      <td>0.506903</td>\n",
       "      <td>0.956884</td>\n",
       "      <td>0.858833</td>\n",
       "      <td>0.878929</td>\n",
       "      <td>0.954023</td>\n",
       "      <td>0.843161</td>\n",
       "      <td>0.627139</td>\n",
       "      <td>0.627139</td>\n",
       "      <td>0.627139</td>\n",
       "      <td>0.679718</td>\n",
       "      <td>0.246512</td>\n",
       "      <td>0.367300</td>\n",
       "      <td>0.678786</td>\n",
       "      <td>0.455695</td>\n",
       "      <td>0.976187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.780544</td>\n",
       "      <td>0.364689</td>\n",
       "      <td>0.930834</td>\n",
       "      <td>0.958909</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915951</td>\n",
       "      <td>0.765281</td>\n",
       "      <td>0.765281</td>\n",
       "      <td>0.765281</td>\n",
       "      <td>0.780544</td>\n",
       "      <td>0.539535</td>\n",
       "      <td>0.192717</td>\n",
       "      <td>0.700858</td>\n",
       "      <td>0.507277</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.764384</td>\n",
       "      <td>0.328480</td>\n",
       "      <td>0.974153</td>\n",
       "      <td>0.790521</td>\n",
       "      <td>0.064942</td>\n",
       "      <td>0.318137</td>\n",
       "      <td>0.982412</td>\n",
       "      <td>0.718826</td>\n",
       "      <td>0.718826</td>\n",
       "      <td>0.718826</td>\n",
       "      <td>0.764384</td>\n",
       "      <td>0.795349</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.828351</td>\n",
       "      <td>0.649195</td>\n",
       "      <td>0.186355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.959237</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980891</td>\n",
       "      <td>0.296445</td>\n",
       "      <td>0.689566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.779951</td>\n",
       "      <td>0.779951</td>\n",
       "      <td>0.767726</td>\n",
       "      <td>0.959237</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.212147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.811544</td>\n",
       "      <td>0.222938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.809973</td>\n",
       "      <td>0.855474</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.626876</td>\n",
       "      <td>0.987223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.284254</td>\n",
       "      <td>0.998826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  本地生产总值（亿人民币）  货物进出口货值（亿人民币）      就业人口    零售业销售额      留宿旅客  \\\n",
       "0   0.000000      0.000000       0.000000  0.000000  0.000000  0.124077   \n",
       "1   0.090909      0.080056       0.136951  0.013763  0.109947  0.218270   \n",
       "2   0.181818      0.184019       0.320233  0.308431  0.233344  0.298858   \n",
       "3   0.272727      0.268372       0.343933  0.450407  0.368112  0.373990   \n",
       "4   0.363636      0.345772       0.232970  0.554868  0.467493  0.477277   \n",
       "5   0.454545      0.441578       0.135108  0.686976  0.568128  0.601422   \n",
       "6   0.545455      0.569715       0.303535  0.816167  0.709005  0.747675   \n",
       "7   0.636364      0.679718       0.506903  0.956884  0.858833  0.878929   \n",
       "8   0.727273      0.780544       0.364689  0.930834  0.958909  1.000000   \n",
       "9   0.818182      0.764384       0.328480  0.974153  0.790521  0.064942   \n",
       "10  0.909091      0.959237       1.000000  1.000000  0.980891  0.296445   \n",
       "11  1.000000      1.000000       0.809973  0.855474  1.000000  0.000000   \n",
       "\n",
       "    人均本地生产总值        人口  第一产业产值（万亿元人民币）  第二产业产值（万亿元人民币）  第三产业产值（万亿元人民币）  \\\n",
       "0   0.000000  0.000000        0.000000        0.000000        0.000000   \n",
       "1   0.183969  0.146235        0.074572        0.074572        0.074572   \n",
       "2   0.443382  0.266778        0.161369        0.161369        0.161369   \n",
       "3   0.556448  0.381350        0.235941        0.235941        0.235941   \n",
       "4   0.394440  0.488450        0.304401        0.304401        0.304401   \n",
       "5   0.491955  0.610306        0.392421        0.392421        0.392421   \n",
       "6   0.758239  0.733150        0.506112        0.506112        0.506112   \n",
       "7   0.954023  0.843161        0.627139        0.627139        0.627139   \n",
       "8   1.000000  0.915951        0.765281        0.765281        0.765281   \n",
       "9   0.318137  0.982412        0.718826        0.718826        0.718826   \n",
       "10  0.689566  1.000000        0.779951        0.779951        0.767726   \n",
       "11  0.626876  0.987223        1.000000        1.000000        1.000000   \n",
       "\n",
       "    本地生产总值(亿元)  博物馆数量(所)  工业增加值占GDP比重(%)  专利申请数(项)  高等学校在校生数(人)  旅游收入(亿元)  \n",
       "0     0.000000  0.000000        1.000000  0.000000     0.000000  0.339518  \n",
       "1     0.080056  0.027907        0.840818  0.039641     0.072052  0.468585  \n",
       "2     0.184019  0.153488        0.816926  0.075848     0.137479  0.588426  \n",
       "3     0.268372  0.200000        0.810017  0.097440     0.204531  0.721916  \n",
       "4     0.345772  0.237209        0.719775  0.185095     0.293649  0.675040  \n",
       "5     0.441578  0.237209        0.554692  0.352139     0.329801  0.702457  \n",
       "6     0.569715  0.241860        0.455239  0.493152     0.422610  0.828052  \n",
       "7     0.679718  0.246512        0.367300  0.678786     0.455695  0.976187  \n",
       "8     0.780544  0.539535        0.192717  0.700858     0.507277  1.000000  \n",
       "9     0.764384  0.795349        0.000000  0.828351     0.649195  0.186355  \n",
       "10    0.959237  0.976744        0.212147  1.000000     0.811544  0.222938  \n",
       "11    1.000000  1.000000        0.284254  0.998826     1.000000  0.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建MinMaxScaler对象\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# 对数据进行归一化处理\n",
    "data_normalized = scaler.fit_transform(data)\n",
    "data_normalized = pd.DataFrame(data_normalized, index=data.index, columns=data.columns)\n",
    "data_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55959395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib auto\n",
    "# 绘制折线图\n",
    "for column in data_normalized.columns:\n",
    "    plt.plot(data_normalized.index.astype(str), data_normalized[column], marker='o', linestyle='-', label=column)\n",
    "\n",
    "# 设置图表标题和坐标轴标签\n",
    "plt.title('各指标随年份变化情况（归一化后）')\n",
    "plt.xlabel('年份')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('归一化数值')\n",
    "\n",
    "# 设置图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c0f136",
   "metadata": {},
   "source": [
    "### 随机森林特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "526f414e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林均方误差（MSE）: 55983976.8147339\n",
      "随机森林决定系数（R²）: 0.8942467016307009\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "plt.rcParams['font.sans-serif'] = ['simHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 读取数据\n",
    "data = pd.read_excel('粤港澳大湾区数据.xlsx')\n",
    "\n",
    "# 将年份设置为索引\n",
    "data.set_index('index', inplace=True)\n",
    "\n",
    "# 自变量和因变量\n",
    "X = data.drop('本地生产总值（亿人民币）', axis=1)\n",
    "y = data['本地生产总值（亿人民币）']\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.35, random_state=42)\n",
    "\n",
    "# 随机森林回归模型\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# 进行预测\n",
    "y_rf_pred = rf_model.predict(X_test)\n",
    "\n",
    "# 计算均方误差（MSE）和决定系数（R²）\n",
    "rf_mse = mean_squared_error(y_test, y_rf_pred)\n",
    "rf_r2 = r2_score(y_test, y_rf_pred)\n",
    "print(\"随机森林均方误差（MSE）:\", rf_mse)\n",
    "print(\"随机森林决定系数（R²）:\", rf_r2)\n",
    "\n",
    "# 确保索引的一致性\n",
    "# 将 y_test 和 y_rf_pred 的索引对应\n",
    "y_test_sorted = y_test.sort_index()  # 对 y_test 按索引排序\n",
    "y_rf_pred_sorted = y_rf_pred[np.argsort(y_test.index)]  # 根据 y_test 的原始索引排序预测值\n",
    "\n",
    "# 绘制预测结果与实际值的对比图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test_sorted.index, y_test_sorted, label='实际值', marker='o')  # 使用排序后的 y_test\n",
    "plt.plot(y_test_sorted.index, y_rf_pred_sorted, label='预测值（随机森林）', linestyle='--')  # 使用排序后的预测值\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('本地生产总值（亿人民币）')\n",
    "plt.title('随机森林模型预测结果')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9194aaa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林模型描述：\n",
      "随机森林均方误差（MSE）: 55983976.8147339\n",
      "随机森林决定系数（R²）: 0.8942467016307009\n",
      "随机森林平均绝对误差（MAE）: 6532.334544181106\n",
      "随机森林中位数绝对误差（MedAE）: 6501.928349999987\n",
      "每个因素及其对应系数：\n",
      "货物进出口货值（亿人民币） : 0.03227140665875707\n",
      "就业人口 : 0.06466694710654076\n",
      "零售业销售额 : 0.03422474888319458\n",
      "留宿旅客 : 0.07071541258213615\n",
      "人均本地生产总值 : 0.07838996667089954\n",
      "人口 : 0.06644853060738903\n",
      "第一产业产值（万亿元人民币） : 0.06687049769373646\n",
      "第二产业产值（万亿元人民币） : 0.04892985747199368\n",
      "第三产业产值（万亿元人民币） : 0.07467071239343563\n",
      "本地生产总值(亿元) : 0.07065619040456769\n",
      "博物馆数量(所) : 0.07244173156424864\n",
      "工业增加值占GDP比重(%) : 0.1603538067493702\n",
      "专利申请数(项) : 0.04127376030488895\n",
      "高等学校在校生数(人) : 0.0707312210939681\n",
      "旅游收入(亿元) : 0.04735520981487365\n",
      "重要因素： ['人均本地生产总值', '工业增加值占GDP比重(%)']\n",
      "较重要因素： ['留宿旅客', '第三产业产值（万亿元人民币）', '本地生产总值(亿元)', '博物馆数量(所)', '高等学校在校生数(人)']\n",
      "一般因素： ['货物进出口货值（亿人民币）', '就业人口', '零售业销售额', '人口', '第一产业产值（万亿元人民币）', '第二产业产值（万亿元人民币）', '专利申请数(项)', '旅游收入(亿元)']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, median_absolute_error\n",
    "\n",
    "# 输出随机森林模型的描述\n",
    "print(\"随机森林模型描述：\")\n",
    "print(\"随机森林均方误差（MSE）:\", rf_mse)\n",
    "print(\"随机森林决定系数（R²）:\", rf_r2)\n",
    "print(\"随机森林平均绝对误差（MAE）:\", mean_absolute_error(y_test, y_rf_pred))\n",
    "print(\"随机森林中位数绝对误差（MedAE）:\", median_absolute_error(y_test, y_rf_pred))\n",
    "\n",
    "# 获取特征重要性并与因素对应\n",
    "feature_importances = rf_model.feature_importances_\n",
    "feature_importance_dict = dict(zip(X.columns, feature_importances))\n",
    "\n",
    "# 输出每个因素及其对应的系数\n",
    "print(\"每个因素及其对应系数：\")\n",
    "for factor, importance in feature_importance_dict.items():\n",
    "    print(factor, \":\", importance)\n",
    "\n",
    "# 根据重要性得分划分因素等级（调整阈值）\n",
    "important_threshold = 0.075\n",
    "less_important_threshold = 0.07\n",
    "important_factors = []\n",
    "less_important_factors = []\n",
    "normal_factors = []\n",
    "\n",
    "for factor, importance in feature_importance_dict.items():\n",
    "    if importance >= important_threshold:\n",
    "        important_factors.append(factor)\n",
    "    elif importance >= less_important_threshold:\n",
    "        less_important_factors.append(factor)\n",
    "    else:\n",
    "        normal_factors.append(factor)\n",
    "\n",
    "print(\"重要因素：\", important_factors)\n",
    "print(\"较重要因素：\", less_important_factors)\n",
    "print(\"一般因素：\", normal_factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec827c99",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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